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Vectors in R^n及向量应用 ℝⁿ 中的向量

本页译自 LibreTexts · A First Course in Linear Algebra (Kuttler)「Vectors in R^n(ℝⁿ 中的向量)」第 4 章各节及习题。公式经 MathJax 渲染,自定义宏已注入;正文与译文段段对照。矩阵/向量用 column 数组形式书写,与教材一致。

学习目标 Outcomes

  1. Find the position vector of a point in $\mathbb{R}^n$.
  1. 求出 $\mathbb{R}^n$ 中某点的位置向量。

The notation $\mathbb{R}^{n}$ refers to the collection of ordered lists of $n$ real numbers, that is $\mathbb{R}^{n} = \left\{ \left( x_{1}, \cdots, x_{n}\right) :x_{j}\in \mathbb{R}\text{ for }j=1,\cdots ,n\right\}$. In this chapter, we take a closer look at vectors in $\mathbb{R}^n$. First, we will consider what $\mathbb{R}^n$ looks like in more detail. Recall that the point given by $0=\left( 0, \cdots, 0 \right)$ is called the origin.

记号 $\mathbb{R}^{n}$ 指的是 $n$ 个实数的有序列表所构成的集合,即 $$\mathbb{R}^{n} = \left\{ \left( x_{1}, \cdots, x_{n}\right) :x_{j}\in \mathbb{R}\text{ for }j=1,\cdots ,n\right\}.$$ 本章我们更仔细地考察 $\mathbb{R}^n$ 中的向量。首先,我们要更详细地了解 $\mathbb{R}^n$ 是什么样子的。回想一下,由 $0=\left( 0, \cdots, 0 \right)$ 给出的那个点称为原点(origin)。

Now, consider the case of $\mathbb{R}^n$ for $n=1.$ Then from the definition we can identify $\mathbb{R}$ with points in $\mathbb{R}^{1}$ as follows: $\mathbb{R} = \mathbb{R}^{1}= \left\{ \left( x_{1}\right) :x_{1}\in \mathbb{R} \right\}$. Hence, $\mathbb{R}$ is defined as the set of all real numbers and geometrically, we can describe this as all the points on a line.

现在来看 $n=1$ 时 $\mathbb{R}^n$ 的情形。根据定义,我们可以把 $\mathbb{R}$ 与 $\mathbb{R}^{1}$ 中的点等同起来: $$\mathbb{R} = \mathbb{R}^{1}= \left\{ \left( x_{1}\right) :x_{1}\in \mathbb{R} \right\}.$$ 因此,$\mathbb{R}$ 被定义为全体实数的集合;从几何上讲,我们可以把它描述为直线上所有的点。

Now suppose $n=2$. Then, from the definition, $\mathbb{R}^{2}= \left\{ \left(x_{1}, x_{2}\right) :x_{j}\in \mathbb{R}\text{ for }j=1,2 \right\}$. Consider the familiar coordinate plane, with an $x$ axis and a $y$ axis. Any point within this coordinate plane is identified by where it is located along the $x$ axis, and also where it is located along the $y$ axis. Consider as an example the following diagram.

再假设 $n=2$。由定义得 $$\mathbb{R}^{2}= \left\{ \left(x_{1}, x_{2}\right) :x_{j}\in \mathbb{R}\text{ for }j=1,2 \right\}.$$ 考虑我们熟悉的坐标平面,它有 $x$ 轴和 $y$ 轴。坐标平面内的任意一点,由它位于 $x$ 轴上的位置以及位于 $y$ 轴上的位置共同确定。下面这张图就是一个例子。
xy 平面中的图形,点 (-3,4) 位于 x=-3、y=4,点 (2,1) 位于 x=2、y=1
Figure 1 图 1

Hence, every element in $\mathbb{R}^2$ is identified by two components, $x$ and $y$, in the usual manner. The coordinates $x, y$ (or $x_1,x_2$) uniquely determine a point in the plan. Note that while the definition uses $x_1$ and $x_2$ to label the coordinates and you may be used to $x$ and $y$, these notations are equivalent.

因此,$\mathbb{R}^2$ 中的每个元素都按通常的方式由两个分量 $x$ 和 $y$ 确定。坐标 $x, y$(或 $x_1,x_2$)唯一地确定了平面中的一个点。注意:虽然定义中用 $x_1$ 和 $x_2$ 来标记坐标,而你或许习惯用 $x$ 和 $y$,但这两套记号是等价的。

Now suppose $n=3$. You may have previously encountered the $3$-dimensional coordinate system, given by $\mathbb{R}^{3}= \left\{ \left( x_{1}, x_{2}, x_{3}\right) :x_{j}\in \mathbb{R}\text{ for }j=1,2,3 \right\}$.

再假设 $n=3$。你可能之前已经接触过三维坐标系,它由下式给出 $$\mathbb{R}^{3}= \left\{ \left( x_{1}, x_{2}, x_{3}\right) :x_{j}\in \mathbb{R}\text{ for }j=1,2,3 \right\}.$$

Points in $\mathbb{R}^3$ will be determined by three coordinates, often written $\left(x,y,z\right)$ which correspond to the $x$, $y$, and $z$ axes. We can think as above that the first two coordinates determine a point in a plane. The third component determines the height above or below the plane, depending on whether this number is positive or negative, and all together this determines a point in space. You see that the ordered triples correspond to points in space just as the ordered pairs correspond to points in a plane and single real numbers correspond to points on a line.

$\mathbb{R}^3$ 中的点由三个坐标确定,通常写成 $\left(x,y,z\right)$,分别对应 $x$、$y$、$z$ 轴。和上面一样,我们可以认为前两个坐标确定了一个平面中的点;第三个分量则根据其正、负决定该点位于平面上方还是下方的高度。所有这些合在一起,就确定了一个空间中的点。你可以看到:有序三元组对应空间中的点,正如有序数对对应平面中的点、单个实数对应直线上的点那样。

The idea behind the more general $\mathbb{R}^n$ is that we can extend these ideas beyond $n = 3.$ This discussion regarding points in $\mathbb{R}^n$ leads into a study of vectors in $\mathbb{R}^n$. While we consider $\mathbb{R}^n$ for all $n$, we will largely focus on $n=2,3$ in this section.

更一般的 $\mathbb{R}^n$ 背后的思想是:我们可以把这些想法推广到 $n > 3$ 的情形。关于 $\mathbb{R}^n$ 中点的这些讨论,将引向对 $\mathbb{R}^n$ 中向量的研究。我们虽然考虑任意的 $n$,但本节将主要聚焦于 $n=2,3$ 的情形。

Consider the following definition.

请看下面的定义。

定义 1:位置向量 The Position Vector

Let $P=\left( p_{1},\cdots ,p_{n}\right)$ be the coordinates of a point in $\mathbb{R}^{n}.$ Then the vector $\overrightarrow{0P}$ with its tail at $0=\left( 0,\cdots ,0\right)$ and its tip at $P$ is called the position vector of the point $P$. We write $$\overrightarrow{0P} = \left[ \begin{array}{c} p_{1} \\ \vdots \\ p_{n} \end{array} \right].$$

设 $P=\left( p_{1},\cdots ,p_{n}\right)$ 是 $\mathbb{R}^{n}$ 中某点的坐标。则以 $0=\left( 0,\cdots ,0\right)$ 为尾、以 $P$ 为尖端向量的向量 $\overrightarrow{0P}$ 称为点 $P$ 的位置向量(position vector)。我们写成 $$\overrightarrow{0P} = \left[ \begin{array}{c} p_{1} \\ \vdots \\ p_{n} \end{array} \right].$$

For this reason we may write both $P=\left( p_{1},\cdots ,p_{n}\right) \in \mathbb{R}^{n}$ and $\overrightarrow{0P} = \left[ p_{1} \cdots p_{n} \right]^T \in \mathbb{R}^{n}$.

因此我们可以同时写 $P=\left( p_{1},\cdots ,p_{n}\right) \in \mathbb{R}^{n}$ 和 $\overrightarrow{0P} = \left[ p_{1} \cdots p_{n} \right]^T \in \mathbb{R}^{n}$。

This definition is illustrated in the following picture for the special case of $\mathbb{R}^{3}$.

在 $\mathbb{R}^{3}$ 的特殊情形下,下图中展示了这一定义。
点 P 及其坐标 (p1,p2,p3) 的三维图,同时标为分量 p1,p2,p3 的向量 0P
Figure 2 图 2

Thus every point $P$ in $\mathbb{R}^{n}$ determines its position vector $\overrightarrow{0P}$. Conversely, every such position vector $\overrightarrow{0P}$ which has its tail at $0$ and point at $P$ determines the point $P$ of $\mathbb{R}^{n}$.

于是 $\mathbb{R}^{n}$ 中的每个点 $P$ 都确定它的位置向量 $\overrightarrow{0P}$。反之,每一个以 $0$ 为尾、以 $P$ 为尖端向量的位置向量 $\overrightarrow{0P}$,也确定了 $\mathbb{R}^{n}$ 中的点 $P$。

Now suppose we are given two points, $P,Q$ whose coordinates are $\left( p_{1},\cdots ,p_{n}\right)$ and $\left( q_{1},\cdots ,q_{n}\right)$ respectively. We can also determine the position vector from $P$ to $Q$ (also called the vector from $P$ to $Q$) defined as follows. $$\overrightarrow{PQ} = \left[ \begin{array}{c} q_{1}-p_{1} \\ \vdots \\ q_{n}-p_{n} \end{array} \right] = \overrightarrow{0Q} - \overrightarrow{0P}.$$

现在假设给定了两个点 $P,Q$,它们的坐标分别是 $\left( p_{1},\cdots ,p_{n}\right)$ 和 $\left( q_{1},\cdots ,q_{n}\right)$。我们还可以确定从 $P$ 到 $Q$ 的位置向量(也称为从 $P$ 到 $Q$ 的向量),定义如下 $$\overrightarrow{PQ} = \left[ \begin{array}{c} q_{1}-p_{1} \\ \vdots \\ q_{n}-p_{n} \end{array} \right] = \overrightarrow{0Q} - \overrightarrow{0P}.$$

Now, imagine taking a vector in $\mathbb{R}^n$ and moving it around, always keeping it pointing in the same direction as shown in the following picture.

现在想象在 $\mathbb{R}^n$ 中取一个向量,并把它四处移动,始终使它指向同一个方向,如下图所示。
三维图:根在原点、分量为 p1,p2,p3 的向量 0P,以及平行但位于别处的从点 A 到点 B 的向量 AB
Figure 3 图 3

After moving it around, it is regarded as the same vector. Each vector, $\overrightarrow{0P}$ and $\overrightarrow{AB}$ has the same length (or magnitude) and direction. Therefore, they are equal.

移动之后,它仍被视作同一个向量。$\overrightarrow{0P}$ 和 $\overrightarrow{AB}$ 这两个向量具有相同的长度(或大小)和方向,因此它们相等。

Consider now the general definition for a vector in $\mathbb{R}^n$.

现在考虑 $\mathbb{R}^n$ 中向量的通用定义。

定义 2:$\mathbb{R}^n$ 中的向量 Vectors in $\mathbb{R}^n$

Let $\mathbb{R}^{n} = \left\{ \left( x_{1}, \cdots, x_{n}\right) :x_{j}\in \mathbb{R}\text{ for }j=1,\cdots ,n\right\} .$ Then, $\vec{x} = \left[ \begin{array}{c} x_{1} \\ \vdots \\ x_{n} \end{array} \right]$ is called a vector. Vectors have both size (magnitude) and direction. The numbers $x_{j}$ are called the components of $\vec{x}$.

设 $\mathbb{R}^{n} = \left\{ \left( x_{1}, \cdots, x_{n}\right) :x_{j}\in \mathbb{R}\text{ for }j=1,\cdots ,n\right\} .$ 则 $\vec{x} = \left[ \begin{array}{c} x_{1} \\ \vdots \\ x_{n} \end{array} \right]$ 称为向量(vector)。向量既有大小(magnitude),也有方向。数 $x_{j}$ 称为 $\vec{x}$ 的分量(components)。

Using this notation, we may use $\vec{p}$ to denote the position vector of point $P$. Notice that in this context, $\vec{p} = \overrightarrow{0P}$. These notations may be used interchangeably.

利用这一记号,我们可以用 $\vec{p}$ 表示点 $P$ 的位置向量。注意在此语境下 $\vec{p} = \overrightarrow{0P}$。这两种记号可以互换使用。

You can think of the components of a vector as directions for obtaining the vector. Consider $n=3$. Draw a vector with its tail at the point $\left( 0,0,0\right)$ and its tip at the point $\left( a,b,c\right)$. This vector it is obtained by starting at $\left( 0,0,0\right)$, moving parallel to the $x$ axis to $\left( a,0,0\right)$ and then from here, moving parallel to the $y$ axis to $\left( a,b,0\right)$ and finally parallel to the $z$ axis to $\left( a,b,c\right).$ Observe that the same vector would result if you began at the point $\left( d,e,f \right)$, moved parallel to the $x$ axis to $\left( d+a,e,f\right) ,$ then parallel to the $y$ axis to $\left( d+a,e+b,f\right) ,$ and finally parallel to the $z$ axis to $\left( d+a,e+b,f+c\right)$. Here, the vector would have its tail sitting at the point determined by $A= \left( d,e,f\right)$ and its point at $B=\left( d+a,e+b,f+c\right) .$ It is the same vector because it will point in the same direction and have the same length. It is like you took an actual arrow, and moved it from one location to another keeping it pointing the same direction.

你可以把向量的分量理解为得到该向量的「行走方向」。考虑 $n=3$:画一个尾在 $\left( 0,0,0\right)$、尖端在 $\left( a,b,c\right)$ 的向量。这个向量的得到方式是:从 $\left( 0,0,0\right)$ 出发,平行于 $x$ 轴移动到 $\left( a,0,0\right)$,然后从这里平行于 $y$ 轴移动到 $\left( a,b,0\right)$,最后平行于 $z$ 轴移动到 $\left( a,b,c\right).$ 可以看到:如果你从点 $\left( d,e,f \right)$ 出发,先平行于 $x$ 轴移到 $\left( d+a,e,f\right) ,$ 再平行于 $y$ 轴移到 $\left( d+a,e+b,f\right) ,$ 最后平行于 $z$ 轴移到 $\left( d+a,e+b,f+c\right)$,同样会得到同一个向量。此时该向量的尾落在由 $A= \left( d,e,f\right)$ 确定的点、尖端落在 $B=\left( d+a,e+b,f+c\right) .$ 它是同一个向量,因为它指向相同的方向而且具有相同的长度。这就好比你真的拿了一支箭,把它从一个位置移到另一个位置,同时始终让它指向同一方向。

We conclude this section with a brief discussion regarding notation. In previous sections, we have written vectors as columns, or $n \times 1$ matrices. For convenience in this chapter we may write vectors as the transpose of row vectors, or $1 \times n$ matrices. These are of course equivalent and we may move between both notations. Therefore, recognize that $\left[ \begin{array}{r} 2 \\ 3 \end{array} \right] = \left[ \begin{array}{rr} 2 & 3 \end{array} \right]^T$.

在结束本节之前,我们简要讨论一下记号。在前几节中,我们把向量写成列向量,即 $n \times 1$ 矩阵。为方便起见,本章中我们可能把向量写成行向量的转置,即 $1 \times n$ 矩阵。这两者当然等价,我们可以在两套记号间来回切换。因此要明白 $$\left[ \begin{array}{r} 2 \\ 3 \end{array} \right] = \left[ \begin{array}{rr} 2 & 3 \end{array} \right]^T.$$

Notice that two vectors $\vec{u} = \left[ u_{1} \cdots u_{n}\right]^T$ and $\vec{v}=\left[ v_{1} \cdots v_{n}\right]^T$ are equal if and only if all corresponding components are equal. Precisely, $$\begin{array}{c} \vec{u}=\vec{v} \text{ if and only if } u_{j}=v_{j} \text{ for all } j=1,\cdots ,n \end{array}.$$ Thus $\left[ \begin{array}{rrr} 1 & 2 & 4 \end{array} \right]^T \in \mathbb{R}^{3}$ and $\left[ \begin{array}{rrr} 2 & 1 & 4 \end{array} \right]^T \in \mathbb{R}^{3}$ but $\left[ \begin{array}{rrr} 1 & 2 & 4 \end{array} \right]^T \neq \left[ \begin{array}{rrr} 2 & 1 & 4 \end{array} \right]^T$ because, even though the same numbers are involved, the order of the numbers is different.

注意:两个向量 $\vec{u} = \left[ u_{1} \cdots u_{n}\right]^T$ 和 $\vec{v}=\left[ v_{1} \cdots v_{n}\right]^T$ 相等,当且仅当所有对应分量都相等。确切地说, $$\begin{array}{c} \vec{u}=\vec{v} \text{ 当且仅当 } u_{j}=v_{j} \text{ 对所有 } j=1,\cdots ,n \text{ 成立} \end{array}.$$ 因此 $\left[ \begin{array}{rrr} 1 & 2 & 4 \end{array} \right]^T \in \mathbb{R}^{3}$ 且 $\left[ \begin{array}{rrr} 2 & 1 & 4 \end{array} \right]^T \in \mathbb{R}^{3}$,但是 $\left[ \begin{array}{rrr} 1 & 2 & 4 \end{array} \right]^T \neq \left[ \begin{array}{rrr} 2 & 1 & 4 \end{array} \right]^T$,因为虽然涉及的数相同,但这些数的次序不同。

For the specific case of $\mathbb{R}^3$, there are three special vectors which we often use. They are given by $\vec{i} = \left[ \begin{array}{rrr} 1 & 0 & 0 \end{array} \right]^T$, $\vec{j} = \left[ \begin{array}{rrr} 0 & 1 & 0 \end{array} \right]^T$, $\vec{k} = \left[ \begin{array}{rrr} 0 & 0 & 1 \end{array} \right]^T$. We can write any vector $\vec{u} = \left[ \begin{array}{rrr} u_1 & u_2 & u_3 \end{array} \right]^T$ as a linear combination of these vectors, written as $\vec{u} = u_1 \vec{i} + u_2 \vec{j} + u_3 \vec{k}$. This notation will be used throughout this chapter.

对于 $\mathbb{R}^3$ 这种特殊情形,有三个常被我们使用的特殊向量,它们由 $$\vec{i} = \left[ \begin{array}{rrr} 1 & 0 & 0 \end{array} \right]^T, \quad \vec{j} = \left[ \begin{array}{rrr} 0 & 1 & 0 \end{array} \right]^T, \quad \vec{k} = \left[ \begin{array}{rrr} 0 & 0 & 1 \end{array} \right]^T$$ 给出。我们可以把任意向量 $\vec{u} = \left[ \begin{array}{rrr} u_1 & u_2 & u_3 \end{array} \right]^T$ 写成这些向量的线性组合,即 $\vec{u} = u_1 \vec{i} + u_2 \vec{j} + u_3 \vec{k}$。本章从头到尾都会用到这一记号。

向量代数 Vector Algebra

学习目标 Outcomes

  1. Understand vector addition and scalar multiplication, algebraically.
  2. Introduce the notion of linear combination of vectors.
  1. 从代数上理解向量加法与标量乘法。
  2. 引入向量的线性组合这一概念。

Addition and scalar multiplication are two important algebraic operations done with vectors. Notice that these operations apply to vectors in $\mathbb{R}^{n}$, for any value of $n$. We will explore these operations in more detail in the following sections.

加法和标量乘法是对向量进行的两种重要代数运算。注意:这些运算适用于 $\mathbb{R}^{n}$ 中的向量,且对任意 $n$ 值都成立。我们将在下面几节更详细地考察这些运算。

$\mathbb{R}^n$ 中向量的加法 Addition of Vectors in $\mathbb{R}^n$

Addition of vectors in $\mathbb{R}^n$ is defined as follows.

$\mathbb{R}^n$ 中向量的加法定义如下。

定义 1:$\mathbb{R}^n$ 中向量的加法 Addition of Vectors in $\mathbb{R}^n$

If $\vec{u}=\left[ \begin{array}{c} u_{1} \\ \vdots \\ u_{n} \end{array} \right],\; \vec{v}= \left[ \begin{array}{c} v_{1} \\ \vdots \\ v_{n} \end{array} \right] \in \mathbb{R}^{n}$, then $\vec{u}+\vec{v}\in \mathbb{R}^{n}$ and is defined by $$\begin{aligned} \vec{u}+\vec{v} &= \left[ \begin{array}{c} u_{1} \\ \vdots \\ u_{n} \end{array} \right] + \left[ \begin{array}{c} v_{1} \\ \vdots \\ v_{n} \end{array} \right] \\ & = \left[ \begin{array}{c} u_{1}+v_{1} \\ \vdots \\ u_{n}+v_{n} \end{array} \right]. \end{aligned}$$

若 $\vec{u}=\left[ \begin{array}{c} u_{1} \\ \vdots \\ u_{n} \end{array} \right],\; \vec{v}= \left[ \begin{array}{c} v_{1} \\ \vdots \\ v_{n} \end{array} \right] \in \mathbb{R}^{n}$,则 $\vec{u}+\vec{v}\in \mathbb{R}^{n}$,其定义为 $$\begin{aligned} \vec{u}+\vec{v} &= \left[ \begin{array}{c} u_{1} \\ \vdots \\ u_{n} \end{array} \right] + \left[ \begin{array}{c} v_{1} \\ \vdots \\ v_{n} \end{array} \right] \\ & = \left[ \begin{array}{c} u_{1}+v_{1} \\ \vdots \\ u_{n}+v_{n} \end{array} \right]. \end{aligned}$$

To add vectors, we simply add corresponding components. Therefore, in order to add vectors, they must be the same size.

要把向量相加,我们只需把对应的分量相加。因此,两个向量要能相加,它们必须大小相同。

Addition of vectors satisfies some important properties which are outlined in the following theorem.

向量的加法满足一些重要性质,这些性质在下面的定理中列出。

定理 1:向量加法的性质 Properties of Vector Addition

The following properties hold for vectors $\vec{u},\vec{v}, \vec{w} \in \mathbb{R}^{n}$.

对向量 $\vec{u},\vec{v}, \vec{w} \in \mathbb{R}^{n}$,下列性质成立。

The additive identity shown above is also called the zero vector, the $n \times 1$ vector in which all components are equal to $0$. Further, $-\vec{u}$ is simply the vector with all components having same value as those of $\vec{u}$ but opposite sign; this is just $(-1)\vec{u}$. This will be made more explicit in the next section when we explore scalar multiplication of vectors. Note that subtraction is defined as $\vec{u}-\vec{v} = \vec{u}+\left( -\vec{v} \right)$.

上式所示的加法单位元也称为零向量(zero vector),即所有分量都等于 $0$ 的那个 $n \times 1$ 向量。进一步,$-\vec{u}$ 就是把 $\vec{u}$ 的所有分量取相反符号、但绝对值与 $\vec{u}$ 各分量相同的那个向量,其实它就是 $(-1)\vec{u}$。在下一节考察向量的标量乘法时,这一点会讲得更明确。注意,减法定义为 $\vec{u}-\vec{v} = \vec{u}+\left( -\vec{v} \right)$。

$\mathbb{R}^n$ 中向量的标量乘法 Scalar Multiplication of Vectors in $\mathbb{R}^n$

Scalar multiplication of vectors in $\mathbb{R}^n$ is defined as follows.

$\mathbb{R}^n$ 中向量的标量乘法定义如下。

定义 2:$\mathbb{R}^n$ 中向量的标量乘法 Scalar Multiplication of Vectors in $\mathbb{R}^n$

If $\vec{u}\in \mathbb{R}^{n}$ and $k\in \mathbb{R}$ is a scalar, then $k\vec{u}\in \mathbb{R}^{n}$ is defined by $k\vec{u}=k\left[ \begin{array}{c} u_{1} \\ \vdots \\ u_{n} \end{array} \right] = \left[ \begin{array}{c} ku_{1} \\ \vdots \\ ku_{n} \end{array} \right]$.

若 $\vec{u}\in \mathbb{R}^{n}$ 且 $k\in \mathbb{R}$ 是一个标量,则 $k\vec{u}\in \mathbb{R}^{n}$ 定义为 $k\vec{u}=k\left[ \begin{array}{c} u_{1} \\ \vdots \\ u_{n} \end{array} \right] = \left[ \begin{array}{c} ku_{1} \\ \vdots \\ ku_{n} \end{array} \right]$。

Just as with addition, scalar multiplication of vectors satisfies several important properties. These are outlined in the following theorem.

与加法一样,向量的标量乘法也满足若干重要性质,列在下面的定理中。

定理 2:标量乘法的性质 Properties of Scalar Multiplication

The following properties hold for vectors $\vec{u},\vec{v}\in \mathbb{R}^{n}$ and $k,p$ scalars.

对向量 $\vec{u},\vec{v}\in \mathbb{R}^{n}$ 以及标量 $k,p$,下列性质成立。

Proof. We will show the proof of $k \left( \vec{u}+\vec{v}\right) = k \vec{u}+ k \vec{v}$. Note that: $$\begin{array}{ll} k \left( \vec{u}+\vec{v}\right) & =k \left[ u_{1}+v_{1} \cdots u_{n}+v_{n}\right]^T \\ & = \left[ k \left( u_{1}+v_{1}\right) \cdots k \left( u_{n}+v_{n}\right) \right]^T \\ & = \left[ k u_{1}+ k v_{1} \cdots k u_{n}+ k v_{n}\right]^T \\ & = \left[ k u_{1} \cdots k u_{n} \right]^T + \left[ k v_{1} \cdots k v_{n} \right]^T \\ & = k \vec{u}+k \vec{v} \end{array}$$

证明。我们来给出 $k \left( \vec{u}+\vec{v}\right) = k \vec{u}+ k \vec{v}$ 的证明。注意: $$\begin{array}{ll} k \left( \vec{u}+\vec{v}\right) & =k \left[ u_{1}+v_{1} \cdots u_{n}+v_{n}\right]^T \\ & = \left[ k \left( u_{1}+v_{1}\right) \cdots k \left( u_{n}+v_{n}\right) \right]^T \\ & = \left[ k u_{1}+ k v_{1} \cdots k u_{n}+ k v_{n}\right]^T \\ & = \left[ k u_{1} \cdots k u_{n} \right]^T + \left[ k v_{1} \cdots k v_{n} \right]^T \\ & = k \vec{u}+k \vec{v} \end{array}$$

We now present a useful notion you may have seen earlier combining vector addition and scalar multiplication.

下面我们介绍一个你可能之前见过的有用概念,它把向量加法和标量乘法结合在一起。

定义 3:线性组合 Linear Combination

A vector $\vec{v}$ is said to be a linear combination of the vectors $\vec{u}_{1},\cdots , \vec{u}_{n}$ if there exist scalars $a_{1},\cdots ,a_{n}$ such that $\vec{v} = a_1 \vec{u}_{1} + \cdots + a_n \vec{u}_{n}$.

若存在标量 $a_{1},\cdots ,a_{n}$,使得 $$\vec{v} = a_1 \vec{u}_{1} + \cdots + a_n \vec{u}_{n},$$ 则称向量 $\vec{v}$ 是向量 $\vec{u}_{1},\cdots , \vec{u}_{n}$ 的线性组合(linear combination)。

For example, $$3 \left[ \begin{array}{r} -4 \\ 1 \\ 0 \end{array} \right] + 2 \left[ \begin{array}{r} -3 \\ 0 \\ 1 \end{array} \right] = \left[ \begin{array}{r} -18 \\ 3 \\ 2 \end{array} \right].$$ Thus we can say that $\vec{v}= \left[ \begin{array}{r} -18 \\ 3 \\ 2 \end{array} \right]$ is a linear combination of the vectors $\vec{u}_{1} = \left[ \begin{array}{r} -4 \\ 1 \\ 0 \end{array} \right]$ and $\vec{u}_{2} = \left[ \begin{array}{r} -3 \\ 0 \\ 1 \end{array} \right]$.

例如, $$3 \left[ \begin{array}{r} -4 \\ 1 \\ 0 \end{array} \right] + 2 \left[ \begin{array}{r} -3 \\ 0 \\ 1 \end{array} \right] = \left[ \begin{array}{r} -18 \\ 3 \\ 2 \end{array} \right].$$ 因此我们可以说,$\vec{v}= \left[ \begin{array}{r} -18 \\ 3 \\ 2 \end{array} \right]$ 是向量 $\vec{u}_{1} = \left[ \begin{array}{r} -4 \\ 1 \\ 0 \end{array} \right]$ 和 $\vec{u}_{2} = \left[ \begin{array}{r} -3 \\ 0 \\ 1 \end{array} \right]$ 的一个线性组合。

向量加法的几何意义 Geometric Meaning of Vector Addition

学习目标 Learning Objectives

  1. Understand vector addition, geometrically.
  1. 从几何上理解向量加法。

Recall that an element of $\mathbb{R}^{n}$ is an ordered list of numbers. For the specific case of $n=2,3$ this can be used to determine a point in two or three dimensional space. This point is specified relative to some coordinate axes.

回想一下:$\mathbb{R}^{n}$ 的一个元素就是一个有序的数列表。对于 $n=2,3$ 的特殊情形,这可以用来确定二维或三维空间中的一个点,而该点相对于某些坐标轴来确定。

Consider the case $n=3$. Recall that taking a vector and moving it around without changing its length or direction does not change the vector. This is important in the geometric representation of vector addition.

考虑 $n=3$ 的情形。回想一下:取一个向量并四处移动它,只要不改变它的长度或方向,向量就保持不变。这一点在向量加法的几何表示中很重要。

Suppose we have two vectors, $\vec{u}$ and $\vec{v}$ in $\mathbb{R}^{3}$. Each of these can be drawn geometrically by placing the tail of each vector at $0$ and its point at $\left( u_{1}, u_{2}, u_{3}\right)$ and $\left( v_{1}, v_{2}, v_{3}\right)$ respectively. Suppose we slide the vector $\vec{v}$ so that its tail sits at the point of $\vec{u}$. We know that this does not change the vector $\vec{v}$. Now, draw a new vector from the tail of $\vec{u}$ to the point of $\vec{v}$. This vector is $\vec{u}+\vec{v}$.

假设我们在 $\mathbb{R}^{3}$ 中有两个向量 $\vec{u}$ 和 $\vec{v}$。把它们各自画出来时,可以把每个向量的尾放在 $0$,把尖端分别放在 $\left( u_{1}, u_{2}, u_{3}\right)$ 和 $\left( v_{1}, v_{2}, v_{3}\right)$。假设我们滑动向量 $\vec{v}$,使它的尾落在 $\vec{u}$ 的尖端。我们知道这不会改变向量 $\vec{v}$。现在,画一条从 $\vec{u}$ 的尾出发、指向 $\vec{v}$ 尖端的新向量,这个向量就是 $\vec{u}+\vec{v}$。

The geometric significance of vector addition in $\mathbb{R}^n$ for any $n$ is given in the following definition.

对任意 $n$,$\mathbb{R}^n$ 中向量加法的几何意义由下面的定义给出。

定义 1:向量加法的几何意义 Geometry of Vector Addition

Let $\vec{u}$ and $\vec{v}$ be two vectors. Slide $\vec{v}$ so that the tail of $\vec{v}$ is on the point of $\vec{u}$. Then draw the arrow which goes from the tail of $\vec{u}$ to the point of $\vec{v}$. This arrow represents the vector $\vec{u}+\vec{v}$.

设 $\vec{u}$ 和 $\vec{v}$ 是两个向量。滑动 $\vec{v}$,使 $\vec{v}$ 的尾落在 $\vec{u}$ 的尖端。然后画一条从 $\vec{u}$ 的尾指向 $\vec{v}$ 尖端的箭头,这条箭头就代表向量 $\vec{u}+\vec{v}$。
二维图:向量 u 指向右,向量 v 从 u 的末端开始指向右上方,向量 u+v 从 u 的起点开始到 v 的末端结束
Figure 4 图 4

This definition is illustrated in the following picture in which $\vec{u}+\vec{v}$ is shown for the special case $n=3$.

在下面这幅图中展示了这一定义,其中 $n=3$ 的特殊情形下示出了 $\vec{u}+\vec{v}$。
三维图:向量 u 和 v 从原点画出,v 的一个拷贝从 u 的末端开始画,u+v 从原点到该拷贝的末端。一条虚线从 u+v 的末端连到原 v 的末端,构成一个平行四边形。
Figure 5 图 5

Notice the parallelogram created by $\vec{u}$ and $\vec{v}$ in the above diagram. Then $\vec{u} + \vec{v}$ is the directed diagonal of the parallelogram determined by the two vectors $\vec{u}$ and $\vec{v}$.

注意上图中由 $\vec{u}$ 和 $\vec{v}$ 构成的平行四边形。$\vec{u} + \vec{v}$ 就是由这两个向量 $\vec{u}$ 和 $\vec{v}$ 确定的平行四边形的有向对角线。

When you have a vector $\vec{v}$, its additive inverse $-\vec{v}$ will be the vector which has the same magnitude as $\vec{v}$ but the opposite direction. When one writes $\vec{u}-\vec{v},$ the meaning is $\vec{u} + \left( -\vec{v}\right)$ as with real numbers. The following example illustrates these definitions and conventions.

当你有一个向量 $\vec{v}$ 时,它的加法逆 $-\vec{v}$ 就是与 $\vec{v}$ 大小相同但方向相反的向量。当写 $\vec{u}-\vec{v}$ 时,它的含义与实数情形一样,是 $\vec{u} + \left( -\vec{v}\right)$。下面的例子说明了这些定义与约定。

例 1:绘制向量加法的图形 Graphing Vector Addition

Consider the following picture of vectors $\vec{u}$ and $\vec{v}$.

考虑下面这幅向量 $\vec{u}$ 和 $\vec{v}$ 的图。
向量 u 指向上右方向,向量 v 指向下右方向
Figure 6 图 6

Sketch a picture of $\vec{u}+\vec{v}$ and $\vec{u}-\vec{v}.$

画出 $\vec{u}+\vec{v}$ 和 $\vec{u}-\vec{v}$ 的草图。

Solution

We will first sketch $\vec{u}+\vec{v}.$ Begin by drawing $\vec{u}$ and then at the point of $\vec{u}$, place the tail of $\vec{v}$ as shown. Then $\vec{u}+\vec{v}$ is the vector which results from drawing a vector from the tail of $\vec{u}$ to the tip of $\vec{v}$.

我们先画 $\vec{u}+\vec{v}.$ 开始时画 $\vec{u}$,然后在 $\vec{u}$ 的尖端放上 $\vec{v}$ 的尾,如图所示。于是 $\vec{u}+\vec{v}$ 就是从 $\vec{u}$ 的尾画到 $\vec{v}$ 尖端所得的向量。
先画出向量 u,然后把 v 的尾放在 u 的尖端。u+v 从 u 的尾画到 v 的尖端。
Figure 7 图 7

Next consider $\vec{u}-\vec{v}.$ This means $\vec{u}+\left( -\vec{v} \right) .$ From the above geometric description of vector addition, $-\vec{v}$ is the vector which has the same length but which points in the opposite direction to $\vec{v}$. Here is a picture.

接下来考虑 $\vec{u}-\vec{v}.$ 它表示 $\vec{u}+\left( -\vec{v} \right) .$ 根据上述向量加法的几何描述,$-\vec{v}$ 是与 $\vec{v}$ 长度相同但指向相反方向的向量。下图就是它的样子。
先画出向量 u。把负的 v 即负 v 的尾放在 u 的尖端,负 v 指向左上。u 减 v 从 u 的尾画到负 v 的尖端。
Figure 8 图 8

向量的长度 Length of a Vector

学习目标 Learning Objectives

In this section, we explore what is meant by the length of a vector in $\mathbb{R}^n$. We develop this concept by first looking at the distance between two points in $\mathbb{R}^n$.

在本节中,我们探讨 $\mathbb{R}^n$ 中向量的长度究竟是什么意思。我们先从考察 $\mathbb{R}^n$ 中两点之间的距离出发,来建立这一概念。

First, we will consider the concept of distance for $\mathbb{R}$, that is, for points in $\mathbb{R}^1$. Here, the distance between two points $P$ and $Q$ is given by the absolute value of their difference. We denote the distance between $P$ and $Q$ by $d(P,Q)$ which is defined as $d(P,Q) = \sqrt{ \left( P-Q\right) ^{2}} $.

首先,我们考虑 $\mathbb{R}$ 即 $\mathbb{R}^1$ 中点的距离概念。这里,两点 $P$ 与 $Q$ 之间的距离由它们之差的绝对值给出。我们把 $P$ 与 $Q$ 之间的距离记为 $d(P,Q)$,其定义为 $d(P,Q) = \sqrt{ \left( P-Q\right) ^{2}} $。

Consider now the case for $n=2$, demonstrated by the following picture.

现在考虑 $n=2$ 的情形,由下面的图来演示。
从坐标 q1,q2 的点 Q 到坐标 p1,p2 的点 P 画出一条直线。一个竖直和水平边构成的方框以这两点为对角顶点,第三个顶点标为 p1,q2
Figure 9 图 9

There are two points $P =\left( p_{1},p_{2}\right)$ and $Q = \left(q_{1},q_{2}\right)$ in the plane. The distance between these points is shown in the picture as a solid line. Notice that this line is the hypotenuse of a right triangle which is half of the rectangle shown in dotted lines. We want to find the length of this hypotenuse which will give the distance between the two points. Note the lengths of the sides of this triangle are $\left| p_{1}-q_{1}\right|$ and $\left| p_{2}-q_{2}\right|$, the absolute value of the difference in these values. Therefore, the Pythagorean Theorem implies the length of the hypotenuse (and thus the distance between $P$ and $Q$) equals $\left( \left| p_{1}-q_{1}\right| ^{2}+\left| p_{2}-q_{2}\right| ^{2}\right) ^{1/2}=\left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}\right) ^{1/2}$.

平面上有两点 $P =\left( p_{1},p_{2}\right)$ 和 $Q = \left(q_{1},q_{2}\right)$。这两点之间的距离在图中用一条实线表示。注意这条实线是一个直角三角形的斜边,而这个直角三角形是虚线所示矩形的一半。我们希望求出这条斜边的长度,它正是两点之间的距离。注意这个三角形两条边的长度分别为 $\left| p_{1}-q_{1}\right|$ 和 $\left| p_{2}-q_{2}\right|$,即这些值之差的绝对值。因此,由勾股定理可知斜边长度(从而正是 $P$ 与 $Q$ 之间的距离)等于 $\left( \left| p_{1}-q_{1}\right| ^{2}+\left| p_{2}-q_{2}\right| ^{2}\right) ^{1/2}=\left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}\right) ^{1/2}$。

Now suppose $n=3$ and let $P = \left( p_{1},p_{2},p_{3}\right)$ and $Q = \left( q_{1},q_{2},q_{3}\right)$ be two points in $\mathbb{R}^{3}.$ Consider the following picture in which the solid line joins the two points and a dotted line joins the points $\left( q_{1},q_{2},q_{3}\right)$ and $\left( p_{1},p_{2},q_{3}\right) .$

现在假设 $n=3$,设 $P = \left( p_{1},p_{2},p_{3}\right)$ 和 $Q = \left( q_{1},q_{2},q_{3}\right)$ 是 $\mathbb{R}^{3}$ 中的两个点。考虑下面的图形,其中实线连接这两个点,虚线连接 $\left( q_{1},q_{2},q_{3}\right)$ 和 $\left( p_{1},p_{2},q_{3}\right) .$
一条三维直线从坐标 q1,q2,q3 的点 Q 画到坐标 p1,p2,p3 的点 P。一个方框以这两个点为对顶点,另外两个角的顶点分别标为 p1,q2,q3 和 p1,p2,q3。
Figure 10 图 10

Here, we need to use Pythagorean Theorem twice in order to find the length of the solid line. First, by the Pythagorean Theorem, the length of the dotted line joining $\left( q_{1},q_{2},q_{3}\right)$ and $\left( p_{1},p_{2},q_{3}\right)$ equals $\left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}\right) ^{1/2}$ while the length of the line joining $\left( p_{1},p_{2},q_{3}\right)$ to $\left( p_{1},p_{2},p_{3}\right)$ is just $\left| p_{3}-q_{3}\right| .$ Therefore, by the Pythagorean Theorem again, the length of the line joining the points $P = \left( p_{1},p_{2},p_{3}\right)$ and $Q = \left( q_{1},q_{2},q_{3}\right)$ equals $\left( \left( \left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}\right) ^{1/2}\right) ^{2}+\left( p_{3}-q_{3}\right) ^{2}\right) ^{1/2}= \left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}+\left( p_{3}-q_{3}\right) ^{2}\right) ^{1/2}$.

这里,为了求出实线的长度,我们需要两次使用勾股定理。首先,由勾股定理,连接 $\left( q_{1},q_{2},q_{3}\right)$ 和 $\left( p_{1},p_{2},q_{3}\right)$ 的虚线长度等于 $\left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}\right) ^{1/2}$,而连接 $\left( p_{1},p_{2},q_{3}\right)$ 与 $\left( p_{1},p_{2},p_{3}\right)$ 的线段长度就是 $\left| p_{3}-q_{3}\right| .$ 因此,再由勾股定理,连接点 $P = \left( p_{1},p_{2},p_{3}\right)$ 与 $Q = \left( q_{1},q_{2},q_{3}\right)$ 的线段长度等于 $$\left( \left( \left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}\right) ^{1/2}\right) ^{2}+\left( p_{3}-q_{3}\right) ^{2}\right) ^{1/2}= \left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}+\left( p_{3}-q_{3}\right) ^{2}\right) ^{1/2}.$$

This discussion motivates the following definition for the distance between points in $\mathbb{R}^n$.

以上讨论引出了 $\mathbb{R}^n$ 中两点之间距离的如下定义。

定义 1:两点之间的距离 Distance Between Points

Let $P=\left( p_{1},\cdots ,p_{n}\right)$ and $Q=\left( q_{1},\cdots ,q_{n}\right)$ be two points in $\mathbb{R}^{n}$. Then the distance between these points is defined as $d( P, Q ) = \left( \sum_{k=1}^{n}\left| p_{k}-q_{k}\right| ^{2}\right) ^{1/2}$. This is called the distance formula. We may also write $\left| P - Q \right|$ as the distance between $P$ and $Q$.

设 $P=\left( p_{1},\cdots ,p_{n}\right)$ 和 $Q=\left( q_{1},\cdots ,q_{n}\right)$ 是 $\mathbb{R}^{n}$ 中的两个点。则这两点之间的距离定义为 $$d( P, Q ) = \left( \sum_{k=1}^{n}\left| p_{k}-q_{k}\right| ^{2}\right) ^{1/2}.$$ 这称为距离公式(distance formula)。我们也可以用 $\left| P - Q \right|$ 表示 $P$ 与 $Q$ 之间的距离。

From the above discussion, you can see that Definition 1 holds for the special cases $n=1,2,3$. In the following example, we use Definition 1 to find the distance between two points in $\mathbb{R}^4$.

由上述讨论你可以看到,定义 1 在特殊情形 $n=1,2,3$ 下成立。在下面的例子中,我们用定义 1 求 $\mathbb{R}^4$ 中两点之间的距离。

例 1:两点之间的距离 Distance Between Points

Find the distance between the points $P$ and $Q$ in $\mathbb{R}^{4}$, where $P$ and $Q$ are given by $P=\left( 1,2,-4,6\right)$ and $Q=\left( 2,3,-1,0\right)$.

求 $\mathbb{R}^{4}$ 中两点 $P$、$Q$ 之间的距离,其中 $P=\left( 1,2,-4,6\right)$,$Q=\left( 2,3,-1,0\right)$。

Solution

We will use the formula given in Definition 1 to find the distance between $P$ and $Q$. Use the distance formula and write $d(P,Q)= \left( \left( 1-2\right) ^{2}+\left( 2-3\right) ^{2}+\left( -4-\left( -1\right) \right) ^{2}+\left( 6-0\right)^{2}\right) ^{\frac{1}{2}} = 47.$

我们使用定义 1 给出的公式来求 $P$ 与 $Q$ 之间的距离。利用距离公式写出 $$d(P,Q)= \left( \left( 1-2\right) ^{2}+\left( 2-3\right) ^{2}+\left( -4-\left( -1\right) \right) ^{2}+\left( 6-0\right)^{2}\right) ^{\frac{1}{2}} = 47.$$

Therefore, $d( P,Q) = \sqrt{47}.$

因此,$d( P,Q) = \sqrt{47}.$

There are certain properties of the distance between points which are important in our study. These are outlined in the following theorem.

两点之间距离的某些性质对我们的研究很重要,这些性质在下面的定理中列出。

定理 1:距离的性质 Properties of Distance

Let $P$ and $Q$ be points in $\mathbb{R}^n$, and let the distance between them, $d( P, Q)$, be given as in Definition 1. Then, the following properties hold.

设 $P$ 和 $Q$ 是 $\mathbb{R}^n$ 中的点,两者之间的距离 $d( P, Q)$ 如定义 1 所给出。则下列性质成立。

There are many applications of the concept of distance. For instance, given two points, we can ask what collection of points are all the same distance between the given points. This is explored in the following example.

距离概念有很多应用。例如,给定两个点,我们可以问:哪些点集上的点到这两个给定点的距离都相等?下面的例子探讨了这一点。

例 2:两平面点 (两给定点之间的平面) The Plane Between Two Points

Describe the points in $\mathbb{R}^3$ which are at the same distance between $\left( 1,2,3\right)$ and $\left( 0,1,2\right) .$

描述 $\mathbb{R}^3$ 中到 $\left( 1,2,3\right)$ 和 $\left( 0,1,2\right)$ 距离相等的那些点。

Solution

Let $P = \left( p_1 , p_2, p_3\right)$ be such a point. Therefore, $P$ is the same distance from $\left( 1,2,3\right)$ and $\left( 0,1,2\right) .$ Then by Definition 1, $\sqrt{\left( p_1 -1\right) ^{2}+\left( p_2 -2\right) ^{2}+\left( p_3-3\right) ^{2}}= \sqrt{\left( p_1 - 0 \right)^{2}+\left( p_2-1\right) ^{2}+\left( p_3-2\right) ^{2}}$. Squaring both sides we obtain $\left( p_1 -1\right) ^{2}+\left( p_2 -2\right) ^{2}+\left( p_3 -3\right) ^{2}=p_1^{2}+\left( p_2-1\right) ^{2}+\left( p_3 -2\right) ^{2}$, and so $p_1^{2}-2p_1+14+p_2^{2}-4p_2+p_3^{2}-6p_3=p_1^{2}+p_2^{2}-2p_2+5+p_3^{2}-4p_3$. Simplifying, this becomes $-2p_1+14-4p_2-6p_3=-2p_2+5-4p_3$, which can be written as $2p_1+2p_2+2p_3=-9$. Therefore, the points $P = \left( p_1,p_2,p_3\right)$ which are the same distance from each of the given points form a plane whose equation is given by $2p_1+2p_2+2p_3=-9$.

设 $P = \left( p_1 , p_2, p_3\right)$ 是这样的一个点。因此 $P$ 到 $\left( 1,2,3\right)$ 和 $\left( 0,1,2\right) .$ 的距离相等。于是由定义 1, $$\sqrt{\left( p_1 -1\right) ^{2}+\left( p_2 -2\right) ^{2}+\left( p_3-3\right) ^{2}}= \sqrt{\left( p_1 - 0 \right)^{2}+\left( p_2-1\right) ^{2}+\left( p_3-2\right) ^{2}}.$$ 两边平方得 $\left( p_1 -1\right) ^{2}+\left( p_2 -2\right) ^{2}+\left( p_3 -3\right) ^{2}=p_1^{2}+\left( p_2-1\right) ^{2}+\left( p_3 -2\right) ^{2}$,于是 $p_1^{2}-2p_1+14+p_2^{2}-4p_2+p_3^{2}-6p_3=p_1^{2}+p_2^{2}-2p_2+5+p_3^{2}-4p_3$。化简后变为 $-2p_1+14-4p_2-6p_3=-2p_2+5-4p_3$,它可以写成 $2p_1+2p_2+2p_3=-9$。因此,到各给定点距离都相等的点 $P = \left( p_1,p_2,p_3\right)$,构成了一个平面,其方程为 $2p_1+2p_2+2p_3=-9$。

We can now use our understanding of the distance between two points to define what is meant by the length of a vector. Consider the following definition.

现在我们可以利用对两点之间距离的理解,来定义向量的长度是什么意思。请看下面的定义。

定义 2:向量的长度 Length of a Vector

Let $\vec{u} = \left[ u_{1} \cdots u_{n} \right]^T$ be a vector in $\mathbb{R}^n$. Then, the length of $\vec{u}$, written $\| \vec{u} \|$, is given by $\| \vec{u} \| = \sqrt{ u_{1}^2 + \cdots + u_{n}^2}$.

设 $\vec{u} = \left[ u_{1} \cdots u_{n} \right]^T$ 是 $\mathbb{R}^n$ 中的一个向量。则 $\vec{u}$ 的长度,记作 $\| \vec{u} \|$,等于 $$\| \vec{u} \| = \sqrt{ u_{1}^2 + \cdots + u_{n}^2}.$$

This definition corresponds to Definition 1, if you consider the vector $\vec{u}$ to have its tail at the point $0 = \left( 0, \cdots ,0 \right)$ and its tip at the point $U = \left(u_1, \cdots, u_n \right)$. Then the length of $\vec{u}$ is equal to the distance between $0$ and $U$, $d(0,U)$. In general, $d(P,Q)=\| \overrightarrow{PQ} \|$.

如果你把向量 $\vec{u}$ 的尾放在点 $0 = \left( 0, \cdots ,0 \right)$、尖端放在点 $U = \left(u_1, \cdots, u_n \right)$,这一定义就与定义 1 相对应。此时 $\vec{u}$ 的长度等于 $0$ 与 $U$ 之间的距离 $d(0,U)$。一般地,$d(P,Q)=\| \overrightarrow{PQ} \|$。

Consider Example 1. By Definition 2, we could also find the distance between $P$ and $Q$ as the length of the vector connecting them. Hence, if we were to draw a vector $\overrightarrow{PQ}$ with its tail at $P$ and its point at $Q$, this vector would have length equal to $\sqrt{47}$.

考虑例 1。按定义 2,我们也可以把 $P$ 与 $Q$ 之间的距离写成连接这两点的向量的长度。因此,如果我们画一个尾在 $P$、尖端在 $Q$ 的向量 $\overrightarrow{PQ}$,这个向量的长度就等于 $\sqrt{47}$。

We conclude this section with a new definition for the special case of vectors of length $1$.

在本节末尾,我们针对长度为 $1$ 的向量这一特殊情形给出一个新定义。

定义 3:单位向量 Unit Vector

Let $\vec{u}$ be a vector in $\mathbb{R}^{n}$. Then, we call $\vec{u}$ a unit vector if it has length $1$, that is if $\| \vec{u} \| = 1$.

设 $\vec{u}$ 是 $\mathbb{R}^{n}$ 中的一个向量。若 $\| \vec{u} \| = 1$,即 $\vec{u}$ 的长度为 $1$,则我们称 $\vec{u}$ 为单位向量(unit vector)。

Let $\vec{v}$ be a vector in $\mathbb{R}^{n}$. Then, the vector $\vec{u}$ which has the same direction as $\vec{v}$ but length equal to $1$ is the corresponding unit vector of $\vec{v}$. This vector is given by $\vec{u} = \frac{1}{\| \vec{v} \|} \vec{v}$.

设 $\vec{v}$ 是 $\mathbb{R}^{n}$ 中的一个向量。则与 $\vec{v}$ 方向相同但长度为 $1$ 的向量 $\vec{u}$,就是 $\vec{v}$ 对应的单位向量,它由 $$\vec{u} = \frac{1}{\| \vec{v} \|} \vec{v}$$ 给出。

We often use the term normalize to refer to this process. When we normalize a vector, we find the corresponding unit vector of length $1$. Consider the following example.

我们常用标准化(normalize)一词来指代这个过程。当我们把向量标准化时,我们就是在求长度为 $1$ 的对应单位向量。请看下面的例子。

例 3:求单位向量 Finding a Unit Vector

Let $\vec{v}$ be given by $\vec{v} = \left[ \begin{array}{rrr} 1 & -3 & 4 \end{array} \right]^T$. Find the unit vector $\vec{u}$ which has the same direction as $\vec{v}$.

设 $\vec{v}$ 由 $\vec{v} = \left[ \begin{array}{rrr} 1 & -3 & 4 \end{array} \right]^T$ 给出。求与 $\vec{v}$ 方向相同的单位向量 $\vec{u}$。

Solution

We will use Definition 3 to solve this. Therefore, we need to find the length of $\vec{v}$ which, by Definition 2, is given by $\| \vec{v} \| = \sqrt{ v_{1}^2 + v_{2}^2+ v_{3}^2}$. Using the corresponding values we find that $$\begin{aligned} \| \vec{v} \| &= \sqrt{ 1^2 + \left(-3 \right)^2 + 4^2} \\ &= \sqrt{ 1 + 9 + 16} \\ &= \sqrt{26} \end{aligned}$$ In order to find $\vec{u}$, we divide $\vec{v}$ by $\sqrt{26}$. The result is $$\begin{aligned} \vec{u} &= \frac{1}{\| \vec{v} \|} \vec{v} \\ &= \frac{1}{\sqrt{26}} \left[ \begin{array}{rrr} 1 & -3 & 4 \end{array} \right]^T \\ &= \left[ \begin{array}{rrr} \frac{1}{\sqrt{26}} & -\frac{3}{\sqrt{26}} & \frac{4}{\sqrt{26}} \end{array} \right]^T \end{aligned}$$

我们用定义 3 来求解。因此我们需要求出 $\vec{v}$ 的长度,按定义 2 它是 $\| \vec{v} \| = \sqrt{ v_{1}^2 + v_{2}^2+ v_{3}^2}$。代入相应数值,求得 $$\begin{aligned} \| \vec{v} \| &= \sqrt{ 1^2 + \left(-3 \right)^2 + 4^2} \\ &= \sqrt{ 1 + 9 + 16} \\ &= \sqrt{26}. \end{aligned}$$ 为求 $\vec{u}$,我们把 $\vec{v}$ 除以 $\sqrt{26}$。结果是 $$\begin{aligned} \vec{u} &= \frac{1}{\| \vec{v} \|} \vec{v} \\ &= \frac{1}{\sqrt{26}} \left[ \begin{array}{rrr} 1 & -3 & 4 \end{array} \right]^T \\ &= \left[ \begin{array}{rrr} \frac{1}{\sqrt{26}} & -\frac{3}{\sqrt{26}} & \frac{4}{\sqrt{26}} \end{array} \right]^T \end{aligned}$$

You can verify using the Definition 1 that $\| \vec{u} \| = 1$.

你可以用定义 1 验证 $\| \vec{u} \| = 1$。

标量乘法的几何意义 Geometric Meaning of Scalar Multiplication

学习目标 Outcomes

  1. Understand scalar multiplication, geometrically.
  1. 从几何上理解标量乘法。

Recall that the point $P=\left( p_{1},p_{2},p_{3}\right)$ determines a vector $\vec{p}$ from $0$ to $P$. The length of $\vec{p}$, denoted $\| \vec{p} \|$, is equal to $\sqrt{p_{1}^{2}+p_{2}^{2}+p_{3}^{2}}$ by Definition 4.4.1.

回想一下:点 $P=\left( p_{1},p_{2},p_{3}\right)$ 确定了从 $0$ 到 $P$ 的一个向量 $\vec{p}$。$\vec{p}$ 的长度记作 $\| \vec{p} \|$,按定义 4.4.1 它等于 $\sqrt{p_{1}^{2}+p_{2}^{2}+p_{3}^{2}}$。

Now suppose we have a vector $\vec{u} = \left[ \begin{array}{lll} u_1 & u_2 & u_3 \end{array} \right]^T$ and we multiply $\vec{u}$ by a scalar $k$. By Definition 4.2.2, $k\vec{u} = \left[ \begin{array}{rrr} ku_{1} & ku_{2} & ku_{3} \end{array} \right]^T$. Then, by using Definition 4.4.1, the length of this vector is given by $\sqrt{\left( \left( k u_{1}\right) ^{2}+\left( k u_{2}\right) ^{2}+\left( k u_{3}\right) ^{2}\right) }=\left| k \right| \sqrt{u_{1}^{2}+u_{2}^{2}+u_{3}^{2}}$. Thus the following holds. $$\| k \vec{u} \| =\left| k \right| \| \vec{u} \|.$$ In other words, multiplication by a scalar magnifies or shrinks the length of the vector by a factor of $\left| k \right|$. If $\left| k \right| > 1$, the length of the resulting vector will be magnified. If $\left| k \right| <1$, the length of the resulting vector will shrink. Remember that by the definition of the absolute value, $\left| k \right| >0$.

现在假设我们有一个向量 $\vec{u} = \left[ \begin{array}{lll} u_1 & u_2 & u_3 \end{array} \right]^T$,并把 $\vec{u}$ 乘以一个标量 $k$。按定义 4.2.2,$k\vec{u} = \left[ \begin{array}{rrr} ku_{1} & ku_{2} & ku_{3} \end{array} \right]^T$。于是,利用定义 4.4.1,这个向量的长度为 $$\sqrt{\left( \left( k u_{1}\right) ^{2}+\left( k u_{2}\right) ^{2}+\left( k u_{3}\right) ^{2}\right) }=\left| k \right| \sqrt{u_{1}^{2}+u_{2}^{2}+u_{3}^{2}}.$$ 于是下式成立: $$\| k \vec{u} \| =\left| k \right| \| \vec{u} \|.$$ 换句话说,乘以一个标量会把向量的长度按 $\left| k \right|$ 这个倍数放大或缩小。若 $\left| k \right| > 1$,所得向量的长度会被放大;若 $\left| k \right| <1$,所得向量的长度会缩小。记住:由绝对值的定义,$\left| k \right| >0$。

What about the direction? Draw a picture of $\vec{u}$ and $k\vec{u}$ where $k$ is negative. Notice that this causes the resulting vector to point in the opposite direction while if $k >0$ it preserves the direction the vector points. Therefore the direction can either reverse, if $k < 0$, or remain preserved, if $k > 0$.

那方向又如何?画一张 $k$ 为负时 $\vec{u}$ 与 $k\vec{u}$ 的图。注意这会使得所得向量指向相反方向;而若 $k >0$,则保持向量所指的方向。因此,方向要么反转(当 $k < 0$ 时),要么保持不变(当 $k > 0$ 时)。

Consider the following example.

请看下面的例子。

例 1:绘制标量乘法的图形 Graphing Scalar Multiplication

Consider the vectors $\vec{u}$ and $\vec{v}$ drawn below.

考虑下面画出的向量 $\vec{u}$ 和 $\vec{v}$。
向量 u 指向右上,向量 v 指向右下
Figure 11 图 11

Draw $-\vec{u}$, $2\vec{v}$, and $-\frac{1}{2}\vec{v}$.

画出 $-\vec{u}$、$2\vec{v}$ 和 $-\frac{1}{2}\vec{v}$。

Solution

In order to find $-\vec{u}$, we preserve the length of $\vec{u}$ and simply reverse the direction. For $2\vec{v}$, we double the length of $\vec{v}$, while preserving the direction. Finally $-\frac{1}{2}\vec{v}$ is found by taking half the length of $\vec{v}$ and reversing the direction. These vectors are shown in the following diagram.

为求出 $-\vec{u}$,我们保持 $\vec{u}$ 的长度、仅把方向反转。对于 $2\vec{v}$,我们把 $\vec{v}$ 的长度加倍,同时保持方向。最后,$-\frac{1}{2}\vec{v}$ 由取 $\vec{v}$ 长度的一半并反转方向得到。这些向量画在下面这张图中。
左边:向量 u 和负 u,长度相同但指向相反。右边:向量 v、2v 指向相同方向但长度加倍、负二分之一 v 指向相反方向且长度减半。
Figure 12 图 12

Now that we have studied both vector addition and scalar multiplication, we can combine the two actions. Recall the definition of linear combinations of column matrices. We can apply this definition to vectors in $\mathbb{R}^n$. A linear combination of vectors in $\mathbb{R}^n$ is a sum of vectors multiplied by scalars.

既然我们已经研究了向量加法和标量乘法,现在可以把这两种运算结合起来。回想一下列矩阵线性组合的定义。我们可以把这一定义应用到 $\mathbb{R}^n$ 中的向量上。$\mathbb{R}^n$ 中向量的线性组合,就是若干向量分别乘以标量后再求和。

In the following example, we examine the geometric meaning of this concept.

在下面的例子中,我们考察这一概念的几何含义。

例 2:绘制向量的线性组合图形 Graphing a Linear Combination of Vectors

Consider the following picture of the vectors $\vec{u}$ and $\vec{v}$.

考虑下面这幅向量 $\vec{u}$ 和 $\vec{v}$ 的图。
向量 u 指向右上,向量 v 指向右下
Figure 13 图 13

Sketch a picture of $\vec{u}+2\vec{v},\vec{u}-\frac{1}{2}\vec{v}.$

画出 $\vec{u}+2\vec{v},\vec{u}-\frac{1}{2}\vec{v}$ 的草图。

Solution

The two vectors are shown below.

这两个向量画在下面。
第一幅:画出向量 u,并画出尾在 u 尖端、长度为 v 两倍的向量 2v。u+2v 从 u 的尾画到 2v 的尖端。第二幅:画出向量 u,并画出尾在 u 尖端、长度为 v 一半且指向相反方向(指向左上)的向量负二分之一 v。u 减二分之一 v 从 u 的尾画到负二分之一 v 的尖端。
Figure 14 图 14

参数直线 Parametric Lines

学习目标 Outcomes

  1. Find the vector and parametric equations of a line.
  1. 求直线的向量方程与参数方程。

We can use the concept of vectors and points to find equations for arbitrary lines in $\mathbb{R}^n$, although in this section the focus will be on lines in $\mathbb{R}^3$.

我们可以利用向量和点的概念来求 $\mathbb{R}^n$ 中任意直线的方程,不过本节的重点放在 $\mathbb{R}^3$ 中的直线上。

To begin, consider the case $n=1$ so we have $\mathbb{R}^{1}=\mathbb{R}$. There is only one line here which is the familiar number line, that is $\mathbb{R}$ itself. Therefore it is not necessary to explore the case of $n=1$ further.

开始时考虑 $n=1$ 的情形,此时 $\mathbb{R}^{1}=\mathbb{R}$。这里只有一条直线,就是我们所熟悉的数轴,也就是 $\mathbb{R}$ 本身。因此没有必要再深入探讨 $n=1$ 的情形。

Now consider the case where $n=2$, in other words $\mathbb{R}^2$. Let $P$ and $P_0$ be two different points in $\mathbb{R}^{2}$ which are contained in a line $L$. Let $\vec{p}$ and $\vec{p_0}$ be the position vectors for the points $P$ and $P_0$ respectively. Suppose that $Q$ is an arbitrary point on $L$. Consider the following diagram.

现在考虑 $n=2$ 的情形,也就是 $\mathbb{R}^2$。设 $P$ 和 $P_0$ 是直线 $L$ 上的两个不同点。设 $\vec{p}$ 和 $\vec{p_0}$ 分别是点 $P$ 和 $P_0$ 的位置向量。假设 $Q$ 是 $L$ 上的任意一点。请看下面的图形。
一条直线上有三个点,分别标为 P0、P 和 Q
Figure 15 图 15

Our goal is to be able to define $Q$ in terms of $P$ and $P_0$. Consider the vector $\overrightarrow{P_0P} = \vec{p} - \vec{p_0}$ which has its tail at $P_0$ and point at $P$. If we add $\vec{p} - \vec{p_0}$ to the position vector $\vec{p_0}$ for $P_0$, the sum would be a vector with its point at $P$. In other words, $\vec{p} = \vec{p_0} + (\vec{p} - \vec{p_0})$.

我们的目标是能够用 $P$ 和 $P_0$ 来定义 $Q$。考虑以 $P_0$ 为尾、以 $P$ 为尖端向量的向量 $\overrightarrow{P_0P} = \vec{p} - \vec{p_0}$。如果把 $\vec{p} - \vec{p_0}$ 加到 $P_0$ 的位置向量 $\vec{p_0}$ 上,所得之和就是一个尖端在 $P$ 的向量。换句话说,$\vec{p} = \vec{p_0} + (\vec{p} - \vec{p_0})$。

Now suppose we were to add $t(\vec{p} - \vec{p_0})$ to $\vec{p}$ where $t$ is some scalar. You can see that by doing so, we could find a vector with its point at $Q$. In other words, we can find $t$ such that $\vec{q} = \vec{p_0} + t \left( \vec{p}- \vec{p_0}\right)$.

现在假设我们把 $t(\vec{p} - \vec{p_0})$ 加到 $\vec{p}$ 上,其中 $t$ 是某个标量。你可以看到,这样做之后,我们就能够找到一个尖端在 $Q$ 的向量。换句话说,我们可以找到 $t$,使得 $$\vec{q} = \vec{p_0} + t \left( \vec{p}- \vec{p_0}\right).$$

This equation determines the line $L$ in $\mathbb{R}^2$. In fact, it determines a line $L$ in $\mathbb{R}^n$. Consider the following definition.

这个方程确定了 $\mathbb{R}^2$ 中的直线 $L$。事实上,它确定了 $\mathbb{R}^n$ 中的一条直线 $L$。请看下面的定义。

定义 1:直线的向量方程 Vector Equation of a Line

Suppose a line $L$ in $\mathbb{R}^{n}$ contains the two different points $P$ and $P_0$. Let $\vec{p}$ and $\vec{p_0}$ be the position vectors of these two points, respectively. Then, $L$ is the collection of points $Q$ which have the position vector $\vec{q}$ given by $\vec{q}=\vec{p_0}+t\left( \vec{p}-\vec{p_0}\right)$ where $t\in \mathbb{R}$.

假设 $\mathbb{R}^{n}$ 中的一条直线 $L$ 含有两个不同的点 $P$ 和 $P_0$。设 $\vec{p}$ 和 $\vec{p_0}$ 分别是这两个点的位置向量。则 $L$ 是那些位置向量 $\vec{q}$ 由 $\vec{q}=\vec{p_0}+t\left( \vec{p}-\vec{p_0}\right)$(其中 $t\in \mathbb{R}$)给出的点 $Q$ 所构成的集合。

Let $\vec{d} = \vec{p} - \vec{p_0}$. Then $\vec{d}$ is the direction vector for $L$ and the vector equation for $L$ is given by $\vec{p}=\vec{p_0}+t\vec{d},\; t\in\mathbb{R}$.

设 $\vec{d} = \vec{p} - \vec{p_0}$。则 $\vec{d}$ 是 $L$ 的方向向量,而 $L$ 的向量方程由 $\vec{p}=\vec{p_0}+t\vec{d},\; t\in\mathbb{R}$ 给出。

Note that this definition agrees with the usual notion of a line in two dimensions and so this is consistent with earlier concepts. Consider now points in $\mathbb{R}^3$. If a point $P \in \mathbb{R}^3$ is given by $P = \left( x,y,z \right)$, $P_0 \in \mathbb{R}^3$ by $P_0 = \left( x_0, y_0, z_0 \right)$, then we can write $\left[ \begin{array}{c} x \\ y \\ z \end{array} \right] = \left[ \begin{array}{c} x_0 \\ y_0 \\ z_0 \end{array} \right] + t \left[ \begin{array}{c} a \\ b \\ c \end{array} \right]$ where $\vec{d} = \left[ \begin{array}{c} a \\ b \\ c \end{array} \right]$. This is the vector equation of $L$ written in component form.

注意:这一定义与二维空间里直线的通常概念一致,因此与之前的概念是相容的。现在考虑 $\mathbb{R}^3$ 中的点。若 $\mathbb{R}^3$ 中的点 $P$ 由 $P = \left( x,y,z \right)$ 给出,$\mathbb{R}^3$ 中的点 $P_0$ 由 $P_0 = \left( x_0, y_0, z_0 \right)$ 给出,那么我们可以写出 $$\left[ \begin{array}{c} x \\ y \\ z \end{array} \right] = \left[ \begin{array}{c} x_0 \\ y_0 \\ z_0 \end{array} \right] + t \left[ \begin{array}{c} a \\ b \\ c \end{array} \right],$$ 其中 $\vec{d} = \left[ \begin{array}{c} a \\ b \\ c \end{array} \right]$。这就是 $L$ 的向量方程,以分量形式(component form)写出。

The following theorem claims that such an equation is in fact a line.

下面的命题声称这样的方程实际上确实是一条直线。

命题 1:直线的代数描述 Algebraic Description of a Straight Line

Let $\vec{a},\vec{b}\in \mathbb{R}^{n}$ with $\vec{b}\neq \vec{0}$. Then $\vec{x}=\vec{a}+t\vec{b},\; t\in \mathbb{R}$, is a line.

设 $\vec{a},\vec{b}\in \mathbb{R}^{n}$ 且 $\vec{b}\neq \vec{0}$。则 $\vec{x}=\vec{a}+t\vec{b},\; t\in \mathbb{R}$ 是一条直线。

证明 Proof

Let $\vec{x_{1}}, \vec{x_{2}} \in \mathbb{R}^n$. Define $\vec{x_{1}}=\vec{a}$ and let $\vec{x_{2}}-\vec{x_{1}}=\vec{b}$. Since $\vec{b} \neq \vec{0}$, it follows that $\vec{x_{2}}\neq \vec{x_{1}}.$ Then $\vec{a}+t\vec{b}=\vec{x_{1}} + t\left( \vec{x_{2}}-\vec{x_{1}}\right)$. It follows that $\vec{x}=\vec{a}+t\vec{b}$ is a line containing the two different points $X_1$ and $X_2$ whose position vectors are given by $\vec{x}_{1}$ and $\vec{x}_{2}$ respectively.

设 $\vec{x_{1}}, \vec{x_{2}} \in \mathbb{R}^n$。定义 $\vec{x_{1}}=\vec{a}$,并令 $\vec{x_{2}}-\vec{x_{1}}=\vec{b}$。由于 $\vec{b} \neq \vec{0}$,可知 $\vec{x_{2}}\neq \vec{x_{1}}.$ 于是 $\vec{a}+t\vec{b}=\vec{x_{1}} + t\left( \vec{x_{2}}-\vec{x_{1}}\right)$。由此可知 $\vec{x}=\vec{a}+t\vec{b}$ 是一条含两个不同点 $X_1$、$X_2$ 的直线,这两个点的位置向量分别由 $\vec{x}_{1}$ 和 $\vec{x}_{2}$ 给出。

We can use the above discussion to find the equation of a line when given two distinct points. Consider the following example.

当给定两个不同的点时,我们可以利用上述讨论来求直线的方程。请看下面的例子。

例 1:由两点确定一条直线 A Line From Two Points

Find a vector equation for the line through the points $P_0 = \left( 1,2,0\right)$ and $P = \left( 2,-4,6\right).$

求经过点 $P_0 = \left( 1,2,0\right)$ 和 $P = \left( 2,-4,6\right)$ 的直线的向量方程。

Solution

We will use the definition of a line given above in Definition 1 to write this line in the form $$\vec{q}=\vec{p_0}+t\left( \vec{p}-\vec{p_0}\right).$$ Let $\vec{q} = \left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B$. Then, we can find $\vec{p}$ and $\vec{p_0}$ by taking the position vectors of points $P$ and $P_0$ respectively. Then, $\vec{q}=\vec{p_0}+t\left( \vec{p}-\vec{p_0}\right)$ can be written as $$\left[ \begin{array}{c} x \\ y \\ z \\ \end{array} \right]^B = \left[ \begin{array}{c} 1 \\ 2 \\ 0 \end{array} \right]^B + t \left[ \begin{array}{r} 1 \\ -6 \\ 6 \end{array} \right]^B, \quad t\in \mathbb{R}.$$ Here, the direction vector $\left[ \begin{array}{r} 1 \\ -6 \\ 6 \end{array} \right]^B$ is obtained by $\vec{p} - \vec{p_0} = \left[ \begin{array}{r} 2 \\ -4 \\ 6 \end{array} \right]^B - \left[ \begin{array}{r} 1 \\ 2 \\ 0 \end{array} \right]^B$ as indicated above in Definition 1.

我们使用上面定义 1 中直线的定义,把这条直线写成 $$\vec{q}=\vec{p_0}+t\left( \vec{p}-\vec{p_0}\right).$$ 设 $\vec{q} = \left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B$。然后,我们可以分别取点 $P$ 和 $P_0$ 的位置向量来求出 $\vec{p}$ 和 $\vec{p_0}$。于是 $\vec{q}=\vec{p_0}+t\left( \vec{p}-\vec{p_0}\right)$ 可以写成 $$\left[ \begin{array}{c} x \\ y \\ z \\ \end{array} \right]^B = \left[ \begin{array}{c} 1 \\ 2 \\ 0 \end{array} \right]^B + t \left[ \begin{array}{r} 1 \\ -6 \\ 6 \end{array} \right]^B, \quad t\in \mathbb{R}.$$ 这里,方向向量 $\left[ \begin{array}{r} 1 \\ -6 \\ 6 \end{array} \right]^B$ 由 $\vec{p} - \vec{p_0} = \left[ \begin{array}{r} 2 \\ -4 \\ 6 \end{array} \right]^B - \left[ \begin{array}{r} 1 \\ 2 \\ 0 \end{array} \right]^B$ 得到,正如上面定义 1 所指出的。

Notice that in the above example we said that we found “a” vector equation for the line, not “the” equation. The reason for this terminology is that there are infinitely many different vector equations for the same line. To see this, replace $t$ with another parameter, say $3s.$ Then you obtain a different vector equation for the same line because the same set of points is obtained.

注意:在上面的例子中,我们说我们找到了这条直线的「一个」向量方程,而不是「那个」方程。之所以用这样的措辞,是因为同一条直线有无限多个不同的向量方程。要明白这一点,把 $t$ 换成另一个参数,比如说 $3s$。这样你就得到了同一条直线的另一个向量方程,因为仍得到同样的点集。

In Example 1, the vector given by $\left[ \begin{array}{r} 1 \\ -6 \\ 6 \end{array} \right]^B$ is the direction vector defined in Definition 1. If we know the direction vector of a line, as well as a point on the line, we can find the vector equation.

在例 1 中,由 $\left[ \begin{array}{r} 1 \\ -6 \\ 6 \end{array} \right]^B$ 给出的向量就是定义 1 中定义的方向向量。如果我们知道一条直线的方向向量,以及直线上的一点,我们就能求出它的向量方程。

Consider the following example.

请看下面的例子。

例 2:由一点和一个方向向量确定直线 A Line From a Point and a Direction Vector

Find a vector equation for the line which contains the point $P_0 = \left( 1,2,0\right)$ and has direction vector $\vec{d} = \left[ \begin{array}{c} 1 \\ 2 \\ 1 \end{array} \right]^B$.

求含点 $P_0 = \left( 1,2,0\right)$ 且方向向量为 $\vec{d} = \left[ \begin{array}{c} 1 \\ 2 \\ 1 \end{array} \right]^B$ 的直线的向量方程。

Solution

We will use Definition 1 to write this line in the form $\vec{p}=\vec{p_0}+t\vec{d},\; t\in \mathbb{R}$. We are given the direction vector $\vec{d}$. In order to find $\vec{p_0}$, we can use the position vector of the point $P_0$. This is given by $\left[ \begin{array}{c} 1 \\ 2 \\ 0 \end{array} \right]^B.$ Letting $\vec{p} = \left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B$, the equation for the line is given by $\left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B = \left[ \begin{array}{c} 1 \\ 2 \\ 0 \end{array} \right]^B + t \left[ \begin{array}{c} 1 \\ 2 \\ 1 \end{array} \right]^B, \; t\in \mathbb{R}$.

我们用定义 1 把这条直线写成 $\vec{p}=\vec{p_0}+t\vec{d},\; t\in \mathbb{R}$ 的形式。我们已经知道了方向向量 $\vec{d}$。为求 $\vec{p_0}$,我们可以利用点 $P_0$ 的位置向量,它由 $\left[ \begin{array}{c} 1 \\ 2 \\ 0 \end{array} \right]^B$ 给出。令 $\vec{p} = \left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B$,则这条直线的方程由 $$\left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B = \left[ \begin{array}{c} 1 \\ 2 \\ 0 \end{array} \right]^B + t \left[ \begin{array}{c} 1 \\ 2 \\ 1 \end{array} \right]^B, \; t\in \mathbb{R}$$ 给出。

We sometimes elect to write a line such as the one given above in the form $$\begin{array}{ll} \left. \begin{array}{l} x=1+t \\ y=2+2t \\ z=t \end{array} \right\} & \mbox{where } t\in \mathbb{R} \end{array}$$ This set of equations give the same information as the vector equation, and is called the parametric equation of the line.

我们有时选择把上面这样的直线写成如下形式 $$\begin{array}{ll} \left. \begin{array}{l} x=1+t \\ y=2+2t \\ z=t \end{array} \right\} & \mbox{其中 } t\in \mathbb{R} \end{array}$$ 这组方程给出的信息与向量方程相同,称为直线的参数方程(parametric equation of the line)。

Consider the following definition.

请看下面的定义。

定义 2:直线的参数方程 Parametric Equation of a Line

Let $L$ be a line in $\mathbb{R}^3$ which has direction vector $\vec{d} = \left[ \begin{array}{c} a \\ b \\ c \end{array} \right]^B$ and goes through the point $P_0 = \left( x_0, y_0, z_0 \right)$. Then, letting $t$ be a parameter, we can write $L$ as $$\begin{array}{ll} \left. \begin{array}{c} x = x_0 + ta \\ y = y_0 + tb \\ z = z_0 + tc \end{array} \right\} & \mbox{where } t\in \mathbb{R} \end{array}$$ This is called a parametric equation of the line $L$.

设 $L$ 是 $\mathbb{R}^3$ 中一条方向向量为 $\vec{d} = \left[ \begin{array}{c} a \\ b \\ c \end{array} \right]^B$、且经过点 $P_0 = \left( x_0, y_0, z_0 \right)$ 的直线。则令 $t$ 为参数,我们可以把 $L$ 写成 $$\begin{array}{ll} \left. \begin{array}{c} x = x_0 + ta \\ y = y_0 + tb \\ z = z_0 + tc \end{array} \right\} & \mbox{其中 } t\in \mathbb{R} \end{array}$$ 这称为直线 $L$ 的参数方程(parametric equation)。

You can verify that the form discussed following Example 2 has the form given in Definition 2.

你可以验证:例 2 之后所讨论的形式正是定义 2 给出的形式。

There is one other form for a line which is useful, which is the symmetric form. Consider the line given by the parametric equation of Example 2. You can solve for the parameter $t$ to write $t=x-1,\; t=\frac{y-2}{2},\; t=z$. Therefore, $x-1=\frac{y-2}{2}=z$. This is the symmetric form of the line.

直线还有一种有用的形式,即对称式(symmetric form)。考虑例 2 的参数方程给出的直线。你可以解出参数 $t$,写成 $t=x-1,\; t=\frac{y-2}{2},\; t=z$。因此 $x-1=\frac{y-2}{2}=z$。这就是该直线的对称式

In the following example, we look at how to take the equation of a line from symmetric form to parametric form.

在下面的例子中,我们看看如何把直线的方程从对称式改写成参数式。

例 3:把对称式改写为参数式 Change Symmetric Form to Parametric Form

Suppose the symmetric form of a line is $\frac{x-2}{3}=\frac{y-1}{2}=z+3$. Write the line in parametric form as well as vector form.

假设一条直线的对称式是 $\frac{x-2}{3}=\frac{y-1}{2}=z+3$。请把这条直线写成参数形式,也写成向量形式。

Solution

We want to write this line in the form given by Definition 2. This is of the form $$\begin{array}{ll} \left. \begin{array}{c} x = x_0 + ta \\ y = y_0 + tb \\ z = z_0 + tc \end{array} \right\} & \mbox{where } t\in \mathbb{R} \end{array}.$$ Let $t=\frac{x-2}{3},t=\frac{y-1}{2}$ and $t=z+3$, as given in the symmetric form of the line. Then solving for $x,y,z,$ yields $$\begin{array}{ll} \left. \begin{array}{c} x=2 + 3t \\ y=1 + 2t \\ z=-3 + t \end{array} \right\} & \mbox{with } t\in \mathbb{R} \end{array}.$$ This is the parametric equation for this line.

我们希望把这条直线写成定义 2 给出的形式,即 $$\begin{array}{ll} \left. \begin{array}{c} x = x_0 + ta \\ y = y_0 + tb \\ z = z_0 + tc \end{array} \right\} & \mbox{其中 } t\in \mathbb{R} \end{array}.$$ 令 $t=\frac{x-2}{3},t=\frac{y-1}{2}$,$t=z+3$,与直线对称式中所给的一致。然后分别解出 $x,y,z$,得到 $$\begin{array}{ll} \left. \begin{array}{c} x=2 + 3t \\ y=1 + 2t \\ z=-3 + t \end{array} \right\} & \mbox{其中 } t\in \mathbb{R} \end{array}.$$ 这就是这条直线的参数方程。

Now, we want to write this line in the form given by Definition 1. This is the form $\vec{p}=\vec{p_0}+t\vec{d}$ where $t\in \mathbb{R}$. This equation becomes $\left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B = \left[ \begin{array}{r} 2 \\ 1 \\ -3 \end{array} \right]^B + t \left[ \begin{array}{r} 3 \\ 2 \\ 1 \end{array} \right]^B, \; t\in \mathbb{R}$.

现在,我们想把这直线写成定义 1 给出的形式,即 $\vec{p}=\vec{p_0}+t\vec{d}$ 的形式,其中 $t\in \mathbb{R}$。这个方程变为 $$\left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B = \left[ \begin{array}{r} 2 \\ 1 \\ -3 \end{array} \right]^B + t \left[ \begin{array}{r} 3 \\ 2 \\ 1 \end{array} \right]^B, \; t\in \mathbb{R}.$$

点积 The Dot Product

学习目标 Outcomes

  1. Compute the dot product of vectors, and use this to compute vector projections.
  1. 计算向量的点积,并利用它计算向量投影。

There are two ways of multiplying vectors which are of great importance in applications. The first of these is called the dot product. When we take the dot product of vectors, the result is a scalar. For this reason, the dot product is also called the scalar product and sometimes the inner product. The definition is as follows.

向量有两种乘法方式,它们在应用中非常重要。第一种称为点积(dot product)。当我们计算向量的点积时,结果是标量。因此,点积也称为数积(scalar product),有时也称内积(inner product)。定义如下。

定义 1:点积 Dot Product

Let $\vec{u},\vec{v}$ be two vectors in $\mathbb{R}^{n}$. Then we define the dot product $\vec{u}\bullet \vec{v}$ as $\vec{u}\bullet \vec{v} = \sum_{k=1}^{n}u_{k}v_{k}$.

设 $\vec{u},\vec{v}$ 是 $\mathbb{R}^{n}$ 中的两个向量。则我们定义点积(dot product)$\vec{u}\bullet \vec{v}$ 为 $\vec{u}\bullet \vec{v} = \sum_{k=1}^{n}u_{k}v_{k}$。

The dot product $\vec{u}\bullet \vec{v}$ is sometimes denoted as $(\vec{u},\vec{v})$ where a comma replaces $\bullet$. It can also be written as $\left\langle \vec{u},\vec{v}\right\rangle$. If we write the vectors as column or row matrices, it is equal to the matrix product $\vec{u}\vec{v}^{T}$.

点积 $\vec{u}\bullet \vec{v}$ 有时记作 $(\vec{u},\vec{v})$,其中用逗号替代 $\bullet$。它也可以写成 $\left\langle \vec{u},\vec{v}\right\rangle$。如果我们把向量写成列矩阵或行矩阵,它就等于矩阵乘积 $\vec{u}\vec{v}^{T}$。

Consider the following example.

请看下面的例子。

例 1:计算一个点积 Compute a Dot Product

Find $\vec{u} \bullet \vec{v}$ for $\vec{u} = \left[ \begin{array}{r} 1 \\ 2 \\ 0 \\ -1 \end{array} \right],\; \vec{v} = \left[ \begin{array}{r} 0 \\ 1 \\ 2 \\ 3 \end{array} \right]$.

对 $\vec{u} = \left[ \begin{array}{r} 1 \\ 2 \\ 0 \\ -1 \end{array} \right],\; \vec{v} = \left[ \begin{array}{r} 0 \\ 1 \\ 2 \\ 3 \end{array} \right]$ 求 $\vec{u} \bullet \vec{v}$。

Solution

By Definition 1, we must compute $\vec{u}\bullet \vec{v} = \sum_{k=1}^{4}u_{k}v_{k}$. This is given by $$\begin{aligned} \vec{u} \bullet \vec{v} &= (1)(0) + (2)(1) + (0)(2) + (-1)(3) \\ &= 0 + 2 + 0 + -3 \\ &= -1. \end{aligned}$$

按定义 1,我们必须计算 $\vec{u}\bullet \vec{v} = \sum_{k=1}^{4}u_{k}v_{k}$。它由下式给出 $$\begin{aligned} \vec{u} \bullet \vec{v} &= (1)(0) + (2)(1) + (0)(2) + (-1)(3) \\ &= 0 + 2 + 0 + -3 \\ &= -1. \end{aligned}$$

With this definition, there are several important properties satisfied by the dot product.

在这个定义下,点积满足若干重要性质。

命题 1:点积的性质 Properties of the Dot Product

Let $k$ and $p$ denote scalars and $\vec{u},\vec{v},\vec{w}$ denote vectors. Then the dot product $\vec{u} \bullet \vec{v}$ satisfies the following properties.

设 $k$、$p$ 表示标量,$\vec{u},\vec{v},\vec{w}$ 表示向量。则点积 $\vec{u} \bullet \vec{v}$ 满足下列性质。

Proof. The proof is left as an exercise.

证明。证明留作练习。

This proposition tells us that we can also use the dot product to find the length of a vector.

这个命题告诉我们,我们也可以用点积来求向量的长度。

例 2:向量的长度 Length of a Vector

Find the length of $\vec{u} = \left[ \begin{array}{r} 2 \\ 1 \\ 4 \\ 2 \end{array} \right]$. That is, find $\| \vec{u} \|.$

求 $\vec{u} = \left[ \begin{array}{r} 2 \\ 1 \\ 4 \\ 2 \end{array} \right]$ 的长度,即求 $\| \vec{u} \|.$

Solution

By Proposition 1, $\| \vec{u} \|^{2} = \vec{u} \bullet \vec{u}$. Therefore, $\| \vec{u} \| = \sqrt {\vec{u} \bullet \vec{u}}$. First, compute $\vec{u} \bullet \vec{u}$. This is given by $$\begin{aligned} \vec{u} \bullet \vec{u} &= (2)(2) + (1)(1) + (4)(4) + (2)(2) \\ &= 4 + 1 + 16 + 4 \\ &= 25. \end{aligned}$$ Then, $$\begin{aligned} \| \vec{u} \| &= \sqrt {\vec{u} \bullet \vec{u}} \\ &= \sqrt{25} \\ &= 5. \end{aligned}$$

按命题 1,$\| \vec{u} \|^{2} = \vec{u} \bullet \vec{u}$。因此 $\| \vec{u} \| = \sqrt {\vec{u} \bullet \vec{u}}$。先计算 $\vec{u} \bullet \vec{u}$。它由下式给出 $$\begin{aligned} \vec{u} \bullet \vec{u} &= (2)(2) + (1)(1) + (4)(4) + (2)(2) \\ &= 4 + 1 + 16 + 4 \\ &= 25. \end{aligned}$$ 于是 $$\begin{aligned} \| \vec{u} \| &= \sqrt {\vec{u} \bullet \vec{u}} \\ &= \sqrt{25} \\ &= 5. \end{aligned}$$

You may wish to compare this to our previous definition of length, given in Definition 4.4.2.

你可以把这与我们之前在定义 4.4.2 中给出的长度定义作比较。

The Cauchy Schwarz inequality is a fundamental inequality satisfied by the dot product. It is given in the following theorem.

柯西-施瓦茨不等式(Cauchy Schwarz inequality)是点积满足的一个基本不等式,在下面的定理中给出。

定理 1:柯西-施瓦茨不等式 Cauchy Schwarz Inequality

The dot product satisfies the inequality $\left\vert \vec{u}\bullet \vec{v}\right\vert \leq \| \vec{u}\| \| \vec{v}\|$. Furthermore equality is obtained if and only if one of $\vec{u}$ or $\vec{v}$ is a scalar multiple of the other.

点积满足不等式 $\left\vert \vec{u}\bullet \vec{v}\right\vert \leq \| \vec{u}\| \| \vec{v}\|$。进一步,取到等号当且仅当 $\vec{u}$、$\vec{v}$ 中有一个是另一个的标量倍。

证明 Proof

First note that if $\vec{v}=\vec{0}$ both sides equal zero and so the inequality holds in this case. Therefore, it will be assumed in what follows that $\vec{v}\neq \vec{0}$.

首先注意:若 $\vec{v}=\vec{0}$,两边都为零,故这种情况下不等式成立。因此,下面将假设 $\vec{v}\neq \vec{0}$。

Define a function of $t\in \mathbb{R}$ by $f\left( t\right) =\left( \vec{u}+t\vec{v}\right) \bullet \left( \vec{u}+ t\vec{v}\right)$. Then by Proposition 1, $f\left( t\right) \geq 0$ for all $t\in \mathbb{R}$. Also from Proposition 1, $$\begin{aligned} f\left( t\right) &=\vec{u}\bullet \left( \vec{u}+t\vec{v}\right) + t\vec{v}\bullet \left( \vec{u}+t\vec{v}\right) \\ &=\vec{u}\bullet \vec{u}+t\left( \vec{u}\bullet \vec{v}\right) + t \vec{v}\bullet \vec{u}+ t^{2}\vec{v}\bullet \vec{v} \\ &=\| \vec{u}\|^{2}+2t\left( \vec{u}\bullet \vec{v}\right) +\| \vec{v}\|^{2}t^{2}. \end{aligned}$$

定义 $t\in \mathbb{R}$ 的函数为 $f\left( t\right) =\left( \vec{u}+t\vec{v}\right) \bullet \left( \vec{u}+ t\vec{v}\right)$。由命题 1,对所有 $t\in \mathbb{R}$ 有 $f\left( t\right) \geq 0$。再根据命题 1, $$\begin{aligned} f\left( t\right) &=\vec{u}\bullet \left( \vec{u}+t\vec{v}\right) + t\vec{v}\bullet \left( \vec{u}+t\vec{v}\right) \\ &=\vec{u}\bullet \vec{u}+t\left( \vec{u}\bullet \vec{v}\right) + t \vec{v}\bullet \vec{u}+ t^{2}\vec{v}\bullet \vec{v} \\ &=\| \vec{u}\|^{2}+2t\left( \vec{u}\bullet \vec{v}\right) +\| \vec{v}\|^{2}t^{2}. \end{aligned}$$

Now this means the graph of $y=f\left( t\right)$ is a parabola which opens up and either its vertex touches the $t$ axis or else the entire graph is above the $t$ axis. In the first case, there exists some $t$ where $f\left( t\right) =0$ and this requires $\vec{u}+t\vec{v}=\vec{0}$ so one vector is a multiple of the other. Then clearly equality holds. In the case where $\vec{v}$ is not a multiple of $\vec{u}$, it follows $f\left( t\right) >0$ for all $t$ which says $f\left( t\right)$ has no real zeros and so from the quadratic formula, $\left( 2\left( \vec{u}\bullet \vec{v}\right) \right) ^{2}-4\| \vec{u}\|^{2}\| \vec{v}\|^{2}<0$, which is equivalent to $\left\vert \vec{u}\bullet \vec{v} \right\vert <\| \vec{u}\| \| \vec{v}\|$.

这意味着 $y=f\left( t\right)$ 的图像是一条开口向上的抛物线,其顶点要么接触 $t$ 轴,要么整条曲线都在 $t$ 轴上方。在第一种情形下,存在某个 $t$ 使 $f\left( t\right) =0$,这需要 $\vec{u}+t\vec{v}=\vec{0}$,于是两个向量中的一个就是另一个的倍数。此时等号显然成立。在 $\vec{v}$ 不是 $\vec{u}$ 的倍数的那种情形下,对所有 $t$ 都有 $f\left( t\right) >0$,这说明 $f\left( t\right)$ 没有实零点,于是由二次方程求根公式,$\left( 2\left( \vec{u}\bullet \vec{v}\right) \right) ^{2}-4\| \vec{u}\|^{2}\| \vec{v}\|^{2}<0$,这等价于 $\left\vert \vec{u}\bullet \vec{v} \right\vert <\| \vec{u}\| \| \vec{v}\|$。

Notice that this proof was based only on the properties of the dot product listed in Proposition 1. This means that whenever an operation satisfies these properties, the Cauchy Schwarz inequality holds. There are many other instances of these properties besides vectors in $\mathbb{R}^{n}$.

注意:这个证明只依赖于命题 1 所列出的点积性质。这意味着,只要某个运算满足这些性质,柯西-施瓦茨不等式就成立。除去 $\mathbb{R}^{n}$ 中的向量之外,满足这些性质的还大有其例。

The Cauchy Schwarz inequality provides another proof of the triangle inequality for distances in $\mathbb{R}^{n}$.

柯西-施瓦茨不等式为 $\mathbb{R}^{n}$ 中距离的三角不等式(triangle inequality)提供了另一个证明。

定理 2:三角不等式 Triangle Inequality

For $\vec{u},\vec{v}\in \mathbb{R}^{n}$, $\| \vec{u}+\vec{v}\| \leq \| \vec{u}\| +\| \vec{v}\|$, and equality holds if and only if one of the vectors is a non-negative scalar multiple of the other.

对 $\vec{u},\vec{v}\in \mathbb{R}^{n}$,$\| \vec{u}+\vec{v}\| \leq \| \vec{u}\| +\| \vec{v}\|$,且取到等号当且仅当两个向量中有一个是另一个的非负标量倍。

Also $\left\| \| \vec{u}\| -\| \vec{v}\| \right\| \leq \| \vec{u}-\vec{v}\|$.

且 $\left\| \| \vec{u}\| -\| \vec{v}\| \right\| \leq \| \vec{u}-\vec{v}\|$。

证明 Proof

By properties of the dot product and the Cauchy Schwarz inequality, $$\begin{aligned} \| \vec{u}+\vec{v}\|^{2} &= \left( \vec{u}+\vec{v}\right) \bullet \left( \vec{u}+\vec{v}\right) \\ & =\left( \vec{u}\bullet \vec{u}\right) +\left( \vec{u}\bullet \vec{v}\right) +\left(\vec{v}\bullet \vec{u}\right) +\left( \vec{v}\bullet \vec{v}\right) \\ &=\| \vec{u}\|^{2}+2\left( \vec{u}\bullet \vec{v}\right)+\| \vec{v}\|^{2} \\ &\leq \| \vec{u}\|^{2}+2\left\vert \vec{u}\bullet \vec{v}\right\vert +\| \vec{v}\|^{2} \\ &\leq \| \vec{u}\|^{2}+2\| \vec{u}\| \| \vec{v}\| +\| \vec{v}\|^{2} =\left( \| \vec{u}\| +\| \vec{v}\|\right) ^{2}. \end{aligned}$$ Hence, $\| \vec{u}+\vec{v}\|^{2} \leq \left( \| \vec{u}\| +\| \vec{v}\| \right) ^{2}$. Taking square roots of both sides you obtain the first inequality.

由点积的性质及柯西-施瓦茨不等式, $$\begin{aligned} \| \vec{u}+\vec{v}\|^{2} &= \left( \vec{u}+\vec{v}\right) \bullet \left( \vec{u}+\vec{v}\right) \\ & =\left( \vec{u}\bullet \vec{u}\right) +\left( \vec{u}\bullet \vec{v}\right) +\left(\vec{v}\bullet \vec{u}\right) +\left( \vec{v}\bullet \vec{v}\right) \\ &=\| \vec{u}\|^{2}+2\left( \vec{u}\bullet \vec{v}\right)+\| \vec{v}\|^{2} \\ &\leq \| \vec{u}\|^{2}+2\left\vert \vec{u}\bullet \vec{v}\right\vert +\| \vec{v}\|^{2} \\ &\leq \| \vec{u}\|^{2}+2\| \vec{u}\| \| \vec{v}\| +\| \vec{v}\|^{2} =\left( \| \vec{u}\| +\| \vec{v}\|\right) ^{2}. \end{aligned}$$ 因此 $\| \vec{u}+\vec{v}\|^{2} \leq \left( \| \vec{u}\| +\| \vec{v}\| \right) ^{2}$。两边开平方,就得到第一个不等式。

It remains to consider when equality occurs. Suppose $\vec{u} = \vec{0}$. Then, $\vec{u} = 0 \vec{v}$ and the claim about when equality occurs is verified. The same argument holds if $\vec{v} = \vec{0}$. Therefore, it can be assumed both vectors are nonzero. To get equality in the first inequality above, Theorem 1 implies one of the vectors must be a multiple of the other. Say $\vec{v}= k \vec{u}$. If $k <0$ then equality cannot occur because in this case $\vec{u}\bullet \vec{v} =k \| \vec{u}\|^{2}<0<\left\| k \right\| \| \vec{u}\|^{2}=\left\| \vec{u}\bullet \vec{v}\right\|$. Therefore, $k \geq 0.$

还需考虑何时取到等号。假设 $\vec{u} = \vec{0}$。则 $\vec{u} = 0 \vec{v}$,等号何时成立的断言便得到验证。若 $\vec{v} = \vec{0}$,同样的论证也成立。因此可以假设两个向量都非零。要在上面第一个不等式中取到等号,定理 1 要求其中一个向量必须是另一个的倍数。设 $\vec{v}= k \vec{u}$。若 $k <0$,则等号不能成立,因为此时 $\vec{u}\bullet \vec{v} =k \| \vec{u}\|^{2}<0<\left\| k \right\| \| \vec{u}\|^{2}=\left\| \vec{u}\bullet \vec{v}\right\|$。因此 $k \geq 0.$

To get the other form of the triangle inequality write $\vec{u}=\vec{u}-\vec{v}+\vec{v}$, so $$\begin{aligned} \| \vec{u}\| &=\| \vec{u}-\vec{v}+\vec{v}\| \\ &\leq \| \vec{u}-\vec{v}\| +\| \vec{v}\| \end{aligned}$$ Therefore, $\| \vec{u}\| -\| \vec{v}\| \leq \| \vec{u}-\vec{v}\|$. Similarly, $\| \vec{v}\| -\| \vec{u}\| \leq \| \vec{v}-\vec{u}\| =\| \vec{u}-\vec{v}\|$. Combining these two inequalities yields the second form of the triangle inequality.

为得到三角不等式的另一形式,把 $\vec{u}$ 写成 $\vec{u}=\vec{u}-\vec{v}+\vec{v}$,于是 $$\begin{aligned} \| \vec{u}\| &=\| \vec{u}-\vec{v}+\vec{v}\| \\ &\leq \| \vec{u}-\vec{v}\| +\| \vec{v}\| \end{aligned}$$ 因此 $\| \vec{u}\| -\| \vec{v}\| \leq \| \vec{u}-\vec{v}\|$。类似地,$\| \vec{v}\| -\| \vec{u}\| \leq \| \vec{v}-\vec{u}\| =\| \vec{u}-\vec{v}\|$。把这两个不等式结合起来,就得到三角不等式的第二种形式。

点积的几何意义 The Geometric Significance of the Dot Product

Given two vectors, $\vec{u}$ and $\vec{v}$, the included angle is the angle between these two vectors which is given by $\theta$ such that $0 \leq \theta \leq \pi$. The dot product can be used to determine the included angle between two vectors. Consider the following picture where $\theta$ gives the included angle.

给定两个向量 $\vec{u}$ 和 $\vec{v}$,夹角(included angle)就是这两个向量之间的角,由 $\theta$ 给出,满足 $0 \leq \theta \leq \pi$。点积可用于确定两个向量之间的夹角。请看下面这幅图,其中 $\theta$ 给出了夹角。
两个向量 u 和 v,向量之间的夹角标为 theta
Figure 16 图 16

命题 2:点积与夹角 The Dot Product and the Included Angle

Let $\vec{u}$ and $\vec{v}$ be two vectors in $\mathbb{R}^n$, and let $\theta$ be the included angle. Then the following equation holds. $\vec{u}\bullet \vec{v}=\| \vec{u}\| \| \vec{v}\| \cos \theta$.

设 $\vec{u}$ 和 $\vec{v}$ 是 $\mathbb{R}^n$ 中的两个向量,$\theta$ 为夹角。则下列等式成立: $$\vec{u}\bullet \vec{v}=\| \vec{u}\| \| \vec{v}\| \cos \theta.$$

In words, the dot product of two vectors equals the product of the magnitude (or length) of the two vectors multiplied by the cosine of the included angle. Note this gives a geometric description of the dot product which does not depend explicitly on the coordinates of the vectors.

用语言说:两个向量的点积等于这两个向量的长度(或大小)之积,再乘以夹角的余弦。注意,这给出了点积的一种几何描述,它并不显式依赖于向量的坐标。

Consider the following example.

请看下面的例子。

例 3:求两个向量之间的夹角 Find the Angle Between Two Vectors

Find the angle between the vectors given by $\vec{u} = \left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right],\; \vec{v} = \left[ \begin{array}{r} 3 \\ 4 \\ 1 \end{array} \right]$.

求由 $\vec{u} = \left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right],\; \vec{v} = \left[ \begin{array}{r} 3 \\ 4 \\ 1 \end{array} \right]$ 给定的两个向量之间的夹角。

Solution

By Proposition 2, $\vec{u}\bullet \vec{v}=\| \vec{u}\| \| \vec{v}\| \cos \theta$. Hence, $\cos \theta =\frac{\vec{u}\bullet \vec{v}}{\| \vec{u}\| \| \vec{v}\|}$.

按命题 2,$\vec{u}\bullet \vec{v}=\| \vec{u}\| \| \vec{v}\| \cos \theta$。因此 $\cos \theta =\frac{\vec{u}\bullet \vec{v}}{\| \vec{u}\| \| \vec{v}\|}$。

First, we can compute $\vec{u}\bullet \vec{v}$. By Definition 1, this equals $\vec{u}\bullet \vec{v} = (2)(3) + (1)(4)+(-1)(1) = 9$. Then, $$\begin{array}{c} \| \vec{u}\| = \sqrt{(2)(2)+(1)(1)+(1)(1)}=\sqrt{6} \\ \| \vec{v}\| = \sqrt{(3)(3)+(4)(4)+(1)(1)}=\sqrt{26} \end{array}$$ Therefore, the cosine of the included angle equals $\cos \theta =\frac{9}{\sqrt{26}\sqrt{6}}=0.7205766...$.

首先,我们可以计算 $\vec{u}\bullet \vec{v}$。按定义 1,它等于 $\vec{u}\bullet \vec{v} = (2)(3) + (1)(4)+(-1)(1) = 9$。于是 $$\begin{array}{c} \| \vec{u}\| = \sqrt{(2)(2)+(1)(1)+(1)(1)}=\sqrt{6} \\ \| \vec{v}\| = \sqrt{(3)(3)+(4)(4)+(1)(1)}=\sqrt{26} \end{array}$$ 因此,夹角的余弦等于 $\cos \theta =\frac{9}{\sqrt{26}\sqrt{6}}=0.7205766...$。

With the cosine known, the angle can be determined by computing the inverse cosine of that angle, giving approximately $\theta =0.76616$ radians.

知道余弦值后,可通过计算该角的反余弦来确定角度,得到 $\theta \approx 0.76616$ 弧度。

Another application of the geometric description of the dot product is in finding the angle between two lines. Typically one would assume that the lines intersect. In some situations, however, it may make sense to ask this question when the lines do not intersect, such as the angle between two object trajectories. In any case we understand it to mean the smallest angle between (any of) their direction vectors. The only subtlety here is that if $\vec{u}$ is a direction vector for a line, then so is any multiple $k\vec{u}$, and thus we will find complementary angles among all angles between direction vectors for two lines, and we simply take the smaller of the two.

点积几何描述的另一个应用是求两条直线之间的夹角。通常人们会假设两条直线相交。然而在某些情形下,当两条直线不相交时,也可能会提出这个问题,例如两条物体运动轨迹之间的夹角。无论何种情况,我们都把它理解为这两条直线的(任意)方向向量之间的最小夹角。这里唯一需要留意的是:如果 $\vec{u}$ 是一条直线的方向向量,那么它的任意倍数 $k\vec{u}$ 也是方向向量,因此我们会在两条直线的方向向量之间的所有夹角中遇到互补的角,而我们只取其中较小的那个。

例 4:求两条直线之间的夹角 Find the Angle Between Two Lines

Find the angle between the two lines $L_1: \left[ \begin{array}{r} x \\ y \\ z \end{array} \right] = \left[ \begin{array}{r} 1 \\ 2 \\ 0 \end{array} \right] +t\left[ \begin{array}{r} -1 \\ 1 \\ 2 \end{array} \right]$ and $L_2: \left[ \begin{array}{r} x \\ y \\ z \end{array} \right] = \left[ \begin{array}{r} 0 \\ 4 \\ -3 \end{array} \right] +s\left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right]$.

求两条直线 $L_1: \left[ \begin{array}{r} x \\ y \\ z \end{array} \right] = \left[ \begin{array}{r} 1 \\ 2 \\ 0 \end{array} \right] +t\left[ \begin{array}{r} -1 \\ 1 \\ 2 \end{array} \right]$ 与 $L_2: \left[ \begin{array}{r} x \\ y \\ z \end{array} \right] = \left[ \begin{array}{r} 0 \\ 4 \\ -3 \end{array} \right] +s\left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right]$ 之间的夹角。

Solution

You can verify that these lines do not intersect, but as discussed above this does not matter and we simply find the smallest angle between any direction vectors for these lines.

你可以验证这两条直线并不相交,但如前面所说这无关紧要,我们只需在这两条直线的任意方向向量之间求最小夹角。

To do so we first find the angle between the direction vectors given above: $\vec{u}=\left[ \begin{array}{r} -1 \\ 1 \\ 2 \end{array} \right],\; \vec{v}=\left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right]$. In order to find the angle, we solve the following equation for $\theta$: $\vec{u}\bullet \vec{v}=\| \vec{u}\| \| \vec{v}\| \cos \theta$, to obtain $\cos \theta = -\frac{1}{2}$, and since we choose included angles between $0$ and $\pi$ we obtain $\theta = \frac{2 \pi}{3}$.

为此,我们首先求上面给出的方向向量之间的夹角:$\vec{u}=\left[ \begin{array}{r} -1 \\ 1 \\ 2 \end{array} \right],\; \vec{v}=\left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right]$。为求夹角,我们对 $\theta$ 解下面的方程:$\vec{u}\bullet \vec{v}=\| \vec{u}\| \| \vec{v}\| \cos \theta$,得到 $\cos \theta = -\frac{1}{2}$,由于我们选取的夹角介于 $0$ 与 $\pi$ 之间,得 $\theta = \frac{2 \pi}{3}$。

Now the angles between any two direction vectors for these lines will either be $\frac{2 \pi}{3}$ or its complement $\phi = \pi - \frac{2 \pi}{3} = \frac{\pi}{3}$. We choose the smaller angle, and therefore conclude that the angle between the two lines is $\frac{\pi}{3}$.

现在,这两条直线的任意两个方向向量之间的夹角,要么是 $\frac{2 \pi}{3}$,要么是它的补角 $\phi = \pi - \frac{2 \pi}{3} = \frac{\pi}{3}$。我们选择较小的角,因此得出结论:这两条直线之间的夹角是 $\frac{\pi}{3}$。

We can also use Proposition 2 to compute the dot product of two vectors.

我们也可以用命题 2 来计算两个向量的点积。

例 5:用几何描述求点积 Using Geometric Description to Find a Dot Product

Let $\vec{u},\vec{v}$ be vectors with $\| \vec{u}\| = 3$ and $\| \vec{v}\| = 4$. Suppose the angle between $\vec{u}$ and $\vec{v}$ is $\pi / 3$. Find $\vec{u}\bullet \vec{v}$.

设 $\vec{u},\vec{v}$ 是向量,且 $\| \vec{u}\| = 3$、$\| \vec{v}\| = 4$。假设 $\vec{u}$ 与 $\vec{v}$ 之间的夹角是 $\pi / 3$。求 $\vec{u}\bullet \vec{v}$。

Solution

From the geometric description of the dot product in Proposition 2, $\vec{u}\bullet \vec{v}=(3)(4) \cos \left( \pi / 3\right) =3\times 4\times 1/2=6$.

由命题 2 中点积的几何描述,$\vec{u}\bullet \vec{v}=(3)(4) \cos \left( \pi / 3\right) =3\times 4\times 1/2=6$。

Two nonzero vectors are said to be perpendicular, sometimes also called orthogonal, if the included angle is $\pi /2$ radians ($90^{\circ }$).

若两个非零向量的夹角是 $\pi /2$ 弧度($90^{\circ }$),则称它们垂直(perpendicular),有时也称正交(orthogonal)。

Consider the following proposition.

请看下面的命题。

命题 3:垂直向量 Perpendicular Vectors

Let $\vec{u}$ and $\vec{v}$ be nonzero vectors in $\mathbb{R}^n$. Then, $\vec{u}$ and $\vec{v}$ are said to be perpendicular exactly when $\vec{u} \bullet \vec{v} = 0$.

设 $\vec{u}$ 和 $\vec{v}$ 是 $\mathbb{R}^n$ 中的非零向量。则称 $\vec{u}$ 和 $\vec{v}$ 垂直,当且仅当 $\vec{u} \bullet \vec{v} = 0$。

证明 Proof

This follows directly from Proposition 2. First if the dot product of two nonzero vectors is equal to $0$, this tells us that $\cos \theta =0$ (this is where we need nonzero vectors). Thus $\theta = \pi /2$ and the vectors are perpendicular.

这直接由命题 2 推出。首先,若两个非零向量的点积等于 $0$,这说明 $\cos \theta =0$(这正是需要非零向量的地方)。于是 $\theta = \pi /2$,向量垂直。

If on the other hand $\vec{v}$ is perpendicular to $\vec{u}$, then the included angle is $\pi /2$ radians. Hence $\cos \theta =0$ and $\vec{u} \bullet \vec{v} = 0$.

另一方面,若 $\vec{v}$ 垂直于 $\vec{u}$,则夹角是 $\pi /2$ 弧度。因此 $\cos \theta =0$,从而 $\vec{u} \bullet \vec{v} = 0$。

Consider the following example.

请看下面的例子。

例 6:判断两个向量是否垂直 Determine if Two Vectors are Perpendicular

Determine whether the two vectors, $\vec{u}= \left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right],\; \vec{v} = \left[ \begin{array}{r} 1 \\ 3 \\ 5 \end{array} \right]$ are perpendicular.

判断两个向量 $\vec{u}= \left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right],\; \vec{v} = \left[ \begin{array}{r} 1 \\ 3 \\ 5 \end{array} \right]$ 是否垂直。

Solution

In order to determine if these two vectors are perpendicular, we compute the dot product. This is given by $\vec{u} \bullet \vec{v} = (2)(1) + (1)(3) + (-1)(5) = 0$. Therefore, by Proposition 3 these two vectors are perpendicular.

为判断这两个向量是否垂直,我们计算它们的点积,得到 $\vec{u} \bullet \vec{v} = (2)(1) + (1)(3) + (-1)(5) = 0$。因此,由命题 3,这两个向量垂直。

投影 Projections

In some applications, we wish to write a vector as a sum of two related vectors. Through the concept of projections, we can find these two vectors. First, we explore an important theorem. The result of this theorem will provide our definition of a vector projection.

在某些应用中,我们希望把一个向量写成两个相关向量的和。通过投影这一概念,我们就可以找到这两个向量。首先,我们探讨一个重要的定理,该定理的结果将为向量投影提供定义。

定理 3:向量投影 Vector Projections

Let $\vec{v}$ and $\vec{u}$ be nonzero vectors. Then there exist unique vectors $\vec{v}_{\|}$ and $\vec{v}_{\bot }$ such that $\vec{v}=\vec{v}_{\|}+\vec{v}_{\bot }$, where $\vec{v}_{\|}$ is a scalar multiple of $\vec{u}$, and $\vec{v}_{\bot}$ is perpendicular to $\vec{u}$.

设 $\vec{v}$ 和 $\vec{u}$ 是非零向量。则存在唯一的一对向量 $\vec{v}_{\|}$ 和 $\vec{v}_{\bot }$,使得 $\vec{v}=\vec{v}_{\|}+\vec{v}_{\bot }$,其中 $\vec{v}_{\|}$ 是 $\vec{u}$ 的标量倍,而 $\vec{v}_{\bot}$ 垂直于 $\vec{u}$。

证明 Proof

Suppose $\vec{v}=\vec{v}_{\|}+\vec{v}_{\bot }$ holds and $\vec{v}_{\|}= k \vec{u}$. Taking the dot product of both sides with $\vec{u}$ and using $\vec{v}_{\bot }\bullet \vec{u}=0,$ this yields $$\begin{array}{ll} \vec{v}\bullet \vec{u} & = ( \vec{v}_{\|}+\vec{v}_{\bot }) \bullet \vec{u} \\ & = k\vec{u} \bullet \vec{u} + \vec{v}_{\bot} \bullet \vec{u} \\ & = k \| \vec{u}\|^{2} \end{array}$$ which requires $k =\vec{v}\bullet \vec{u} / \| \vec{u}\|^{2}.$ Thus there can be no more than one vector $\vec{v}_{\|}$. It follows $\vec{v}_{\bot }$ must equal $\vec{v}-\vec{v}_{\|}.$ This verifies there can be no more than one choice for both $\vec{v}_{\|}$ and $\vec{v}_{\bot }$ and proves their uniqueness.

假设 $\vec{v}=\vec{v}_{\|}+\vec{v}_{\bot }$ 成立,且 $\vec{v}_{\|}= k \vec{u}$。两边与 $\vec{u}$ 作点积,并利用 $\vec{v}_{\bot }\bullet \vec{u}=0$,得 $$\begin{array}{ll} \vec{v}\bullet \vec{u} & = ( \vec{v}_{\|}+\vec{v}_{\bot }) \bullet \vec{u} \\ & = k\vec{u} \bullet \vec{u} + \vec{v}_{\bot} \bullet \vec{u} \\ & = k \| \vec{u}\|^{2}, \end{array}$$ 这就需要 $k =\vec{v}\bullet \vec{u} / \| \vec{u}\|^{2}.$ 因此向量 $\vec{v}_{\|}$ 至多有一个。由此 $\vec{v}_{\bot }$ 必等于 $\vec{v}-\vec{v}_{\|}.$ 这就验证了 $\vec{v}_{\|}$ 与 $\vec{v}_{\bot }$ 至多各有一种选取方式,证明了它们的唯一性。

Now let $\vec{v}_{\|} = \frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}\vec{u}$ and let $\vec{v}_{\bot }=\vec{v}-\vec{v}_{\|}=\vec{v}-\frac{\vec{v}\bullet \vec{u}} {\| \vec{u}\|^{2}}\vec{u}$. Then $\vec{v}_{\|}= k\vec{u}$ where $k =\frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}$. It only remains to verify $\vec{v}_{\bot }\bullet \vec{u}=0.$ But $$\begin{aligned} \vec{v}_{\bot }\bullet \vec{u} &= \vec{v}\bullet \vec{u}-\frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}\vec{u}\bullet \vec{u} \\ &= \vec{v}\bullet\vec{u}-\vec{v}\bullet \vec{u} \\ &= 0. \end{aligned}$$

现在令 $\vec{v}_{\|} = \frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}\vec{u}$,并令 $\vec{v}_{\bot }=\vec{v}-\vec{v}_{\|}=\vec{v}-\frac{\vec{v}\bullet \vec{u}} {\| \vec{u}\|^{2}}\vec{u}$。则 $\vec{v}_{\|}= k\vec{u}$,其中 $k =\frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}$。只需再验证 $\vec{v}_{\bot }\bullet \vec{u}=0.$ 但 $$\begin{aligned} \vec{v}_{\bot }\bullet \vec{u} &= \vec{v}\bullet \vec{u}-\frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}\vec{u}\bullet \vec{u} \\ &= \vec{v}\bullet\vec{u}-\vec{v}\bullet \vec{u} \\ &= 0. \end{aligned}$$

The vector $\vec{v}_{\|}$ in Theorem 3 is called the projection of $\vec{v}$ onto $\vec{u}$ and is denoted by $\vec{v}_{\|} = \mathrm{proj}_{\vec{u}}\left( \vec{v}\right)$.

定理 3 中的向量 $\vec{v}_{\|}$ 称为 $\vec{v}$ 在 $\vec{u}$ 上的投影(projection),记作 $\vec{v}_{\|} = \mathrm{proj}_{\vec{u}}\left( \vec{v}\right)$。

We now make a formal definition of the vector projection.

下面我们给出向量投影的形式定义。

定义 2:向量投影 Vector Projection

Let $\vec{u}$ and $\vec{v}$ be vectors. Then, the projection of $\vec{v}$ onto $\vec{u}$ is given by $\mathrm{proj}_{\vec{u}}\left( \vec{v}\right) =\left( \frac{\vec{v}\bullet \vec{u}}{\vec{u}\bullet \vec{u}}\right) \vec{u} = \frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}\vec{u}$.

设 $\vec{u}$ 和 $\vec{v}$ 是向量。则 $\vec{v}$ 在 $\vec{u}$ 上的投影由 $$\mathrm{proj}_{\vec{u}}\left( \vec{v}\right) =\left( \frac{\vec{v}\bullet \vec{u}}{\vec{u}\bullet \vec{u}}\right) \vec{u} = \frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}\vec{u}$$ 给出。

Consider the following example of a projection.

请看下面一个投影的例子。

例 7:求一个向量在另一个向量上的投影 Find the Projection of One Vector Onto Another

Find $\mathrm{proj}_{\vec{u}}\left( \vec{v}\right)$ if $\vec{u}= \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right],\; \vec{v}= \left[ \begin{array}{r} 1 \\ -2 \\ 1 \end{array} \right]$.

若 $\vec{u}= \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right],\; \vec{v}= \left[ \begin{array}{r} 1 \\ -2 \\ 1 \end{array} \right]$,求 $\mathrm{proj}_{\vec{u}}\left( \vec{v}\right)$。

Solution

We can use the formula provided in Definition 2 to find $\mathrm{proj}_{\vec{u}}\left( \vec{v}\right)$. First, compute $\vec{v} \bullet \vec{u}$. This is given by $$\begin{aligned} \left[ \begin{array}{r} 1 \\ -2 \\ 1 \end{array} \right] \bullet \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right] &= (2)(1) + (3)(-2) + (-4)(1) \\ &= 2 - 6 - 4 \\ &= -8. \end{aligned}$$ Similarly, $\vec{u} \bullet \vec{u}$ is given by $$\begin{aligned} \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right] \bullet \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right] &= (2)(2) + (3)(3) + (-4)(-4) \\ &= 4 + 9 + 16 \\ &= 29. \end{aligned}$$ Therefore, the projection is equal to $$\begin{aligned} \mathrm{proj}_{\vec{u}}\left( \vec{v}\right) &=-\frac{8}{29} \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right] \\ &= \left[ \begin{array}{r} - \frac{16}{29} \\ - \frac{24}{29} \\ \frac{32}{29} \end{array} \right]. \end{aligned}$$

我们可以用定义 2 中给出的公式来求 $\mathrm{proj}_{\vec{u}}\left( \vec{v}\right)$。先计算 $\vec{v} \bullet \vec{u}$,它为 $$\begin{aligned} \left[ \begin{array}{r} 1 \\ -2 \\ 1 \end{array} \right] \bullet \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right] &= (2)(1) + (3)(-2) + (-4)(1) \\ &= 2 - 6 - 4 \\ &= -8. \end{aligned}$$ 类似地,$\vec{u} \bullet \vec{u}$ 为 $$\begin{aligned} \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right] \bullet \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right] &= (2)(2) + (3)(3) + (-4)(-4) \\ &= 4 + 9 + 16 \\ &= 29. \end{aligned}$$ 因此,投影等于 $$\begin{aligned} \mathrm{proj}_{\vec{u}}\left( \vec{v}\right) &=-\frac{8}{29} \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right] \\ &= \left[ \begin{array}{r} - \frac{16}{29} \\ - \frac{24}{29} \\ \frac{32}{29} \end{array} \right]. \end{aligned}$$

We will conclude this section with an important application of projections. Suppose a line $L$ and a point $P$ are given such that $P$ is not contained in $L$. Through the use of projections, we can determine the shortest distance from $P$ to $L$.

在结束本节前,我们介绍投影的一个重要应用。假设给定一条直线 $L$ 和一个点 $P$,且 $P$ 不在 $L$ 上。利用投影,我们可以求出从 $P$ 到 $L$ 的最短距离。

例 8:从一点到一条直线的最短距离 Shortest Distance from a Point to a Line

Let $P = (1,3,5)$ be a point in $\mathbb{R}^3$, and let $L$ be the line which goes through point $P_0 = (0,4,-2)$ with direction vector $\vec{d} = \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right]$. Find the shortest distance from $P$ to the line $L$, and find the point $Q$ on $L$ that is closest to $P$.

设 $P = (1,3,5)$ 是 $\mathbb{R}^3$ 中的一个点,$L$ 是经过点 $P_0 = (0,4,-2)$、方向向量为 $\vec{d} = \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right]$ 的直线。求从 $P$ 到直线 $L$ 的最短距离,并求 $L$ 上离 $P$ 最近的点 $Q$。

Solution

In order to determine the shortest distance from $P$ to $L$, we will first find the vector $\overrightarrow{P_0P}$ and then find the projection of this vector onto $L$. The vector $\overrightarrow{P_0P}$ is given by $\left[ \begin{array}{r} 1 \\ 3 \\ 5 \end{array} \right] - \left[ \begin{array}{r} 0 \\ 4 \\ -2 \end{array} \right] = \left[ \begin{array}{r} 1 \\ -1 \\ 7 \end{array} \right]$.

为确定从 $P$ 到 $L$ 的最短距离,我们首先求向量 $\overrightarrow{P_0P}$,然后求该向量在 $L$ 上的投影。向量 $\overrightarrow{P_0P}$ 由 $\left[ \begin{array}{r} 1 \\ 3 \\ 5 \end{array} \right] - \left[ \begin{array}{r} 0 \\ 4 \\ -2 \end{array} \right] = \left[ \begin{array}{r} 1 \\ -1 \\ 7 \end{array} \right]$ 给出。

Then, if $Q$ is the point on $L$ closest to $P$, it follows that $$\begin{aligned} \overrightarrow{P_0Q} &= \mathrm{proj}_{\vec{d}}\overrightarrow{P_0P} \\ &= \left( \frac{ \overrightarrow{P_0P}\bullet \vec{d}}{\| \vec{d}\|^2}\right) \vec{d} \\ &= \frac{15}{9} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right] \\ &= \frac{5}{3} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right]. \end{aligned}$$

那么,若 $Q$ 是 $L$ 上离 $P$ 最近的点,则 $$\begin{aligned} \overrightarrow{P_0Q} &= \mathrm{proj}_{\vec{d}}\overrightarrow{P_0P} \\ &= \left( \frac{ \overrightarrow{P_0P}\bullet \vec{d}}{\| \vec{d}\|^2}\right) \vec{d} \\ &= \frac{15}{9} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right] \\ &= \frac{5}{3} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right]. \end{aligned}$$

Now, the distance from $P$ to $L$ is given by $\| \overrightarrow{QP} \| = \| \overrightarrow{P_0P} - \overrightarrow{P_0Q}\| = \sqrt{26}$.

现在,从 $P$ 到 $L$ 的距离由 $\| \overrightarrow{QP} \| = \| \overrightarrow{P_0P} - \overrightarrow{P_0Q}\| = \sqrt{26}$ 给出。

The point $Q$ is found by adding the vector $\overrightarrow{P_0Q}$ to the position vector $\overrightarrow{0P_0}$ for $P_0$ as follows $$\begin{aligned} \left[ \begin{array}{r} 0 \\ 4 \\ -2 \end{array} \right] + \frac{5}{3} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right] &= \left[ \begin{array}{r} \frac{10}{3} \\ \frac{17}{3} \\ \frac{4}{3} \end{array} \right]. \end{aligned}$$ Therefore, $Q = (\frac{10}{3}, \frac{17}{3}, \frac{4}{3})$.

点 $Q$ 通过把向量 $\overrightarrow{P_0Q}$ 加到 $P_0$ 的位置向量 $\overrightarrow{0P_0}$ 上来求得,如下: $$\begin{aligned} \left[ \begin{array}{r} 0 \\ 4 \\ -2 \end{array} \right] + \frac{5}{3} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right] &= \left[ \begin{array}{r} \frac{10}{3} \\ \frac{17}{3} \\ \frac{4}{3} \end{array} \right]. \end{aligned}$$ 因此,$Q = (\frac{10}{3}, \frac{17}{3}, \frac{4}{3})$。

平面 Planes in $\mathbb{R}^n$

学习目标 Outcomes

  1. Find the vector and scalar equations of a plane.
  1. 求平面的向量方程与数量方程。

Much like the above discussion with lines, vectors can be used to determine planes in $\mathbb{R}^n$. Given a vector $\vec{n}$ in $\mathbb{R}^n$ and a point $P_0$, it is possible to find a unique plane which contains $P_0$ and is perpendicular to the given vector.

与上面关于直线的讨论十分相似,向量也可以用来确定 $\mathbb{R}^n$ 中的平面。给定 $\mathbb{R}^n$ 中的一个向量 $\vec{n}$ 和一个点 $P_0$,可以找到一个唯一的平面,它包含 $P_0$ 且垂直于给定的向量。

定义 1:法向量 Normal Vector

Let $\vec{n}$ be a nonzero vector in $\mathbb{R}^n$. Then $\vec{n}$ is called a normal vector to a plane if and only if $\vec{n} \bullet \vec{v} = 0$ for every vector $\vec{v}$ in the plane.

设 $\vec{n}$ 是 $\mathbb{R}^n$ 中的一个非零向量。若对平面内的每个向量 $\vec{v}$ 都有 $\vec{n} \bullet \vec{v} = 0$,则称 $\vec{n}$ 是该平面的法向量(normal vector)。

In other words, we say that $\vec{n}$ is orthogonal (perpendicular) to every vector in the plane.

换句话说,我们说 $\vec{n}$ 与该平面内的每个向量都正交(垂直)。

Consider now a plane with normal vector given by $\vec{n}$, and containing a point $P_0$. Notice that this plane is unique. If $P$ is an arbitrary point on this plane, then by definition the normal vector is orthogonal to the vector between $P_0$ and $P$. Letting $\overrightarrow{0P}$ and $\overrightarrow{0P_0}$ be the position vectors of points $P$ and $P_0$ respectively, it follows that $\vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) = 0$ or $\vec{n} \bullet \overrightarrow{P_0P} = 0$.

现在考虑一个法向量为 $\vec{n}$、且包含点 $P_0$ 的平面。注意这个平面是唯一的。若 $P$ 是该平面上的任意一点,那么按定义,法向量与 $P_0$ 和 $P$ 之间的向量正交。设 $\overrightarrow{0P}$ 和 $\overrightarrow{0P_0}$ 分别是点 $P$ 和 $P_0$ 的位置向量,则有 $\vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) = 0$ 或 $\vec{n} \bullet \overrightarrow{P_0P} = 0$。

The first of these equations gives the vector equation of the plane.

其中第一个方程给出了平面的向量方程(vector equation)。

定义 2:平面的向量方程 Vector Equation of a Plane

Let $\vec{n}$ be the normal vector for a plane which contains a point $P_0$. If $P$ is an arbitrary point on this plane, then the vector equation of the plane is given by $\vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) = 0$.

设 $\vec{n}$ 是包含点 $P_0$ 的某个平面的法向量。若 $P$ 是该平面上的任意一点,则平面的向量方程由 $\vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) = 0$ 给出。

Notice that this equation can be used to determine if a point $P$ is contained in a certain plane.

注意:这个方程可以用来判断一个点 $P$ 是否位于某个平面内。

例 1:平面中的一点 A Point in a Plane

Let $\vec{n} = \left[ \begin{array}{r} 1 \\ 2 \\ 3 \end{array} \right]$ be the normal vector for a plane which contains the point $P_0 = \left( 2, 1, 4 \right)$. Determine if the point $P = \left( 5, 4, 1 \right)$ is contained in this plane.

设 $\vec{n} = \left[ \begin{array}{r} 1 \\ 2 \\ 3 \end{array} \right]$ 是包含点 $P_0 = \left( 2, 1, 4 \right)$ 的某个平面的法向量。判断点 $P = \left( 5, 4, 1 \right)$ 是否位于这个平面内。

Solution

By Definition 2, $P$ is a point in the plane if it satisfies the equation $\vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) = 0$. Given the above $\vec{n}$, $P_0$, and $P$, this equation becomes $$\begin{aligned} \left[ \begin{array}{r} 1 \\ 2 \\ 3 \end{array} \right] \bullet \left( \left[ \begin{array}{r} 5 \\ 4 \\ 1 \end{array} \right] - \left[ \begin{array}{r} 2 \\ 1 \\ 4 \end{array} \right] \right) &= \left[ \begin{array}{r} 1 \\ 2 \\ 3 \end{array} \right] \bullet \left( \left[ \begin{array}{r} 3 \\ 3 \\ -3 \end{array} \right] \right) \\ &= 3 + 6 - 9 = 0. \end{aligned}$$ Therefore $P = ( 5, 4, 1)$ is contained in the plane.

按定义 2,若 $P$ 满足方程 $\vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) = 0$,则它是平面内的点。代入上述 $\vec{n}$、$P_0$、$P$,这个方程变为 $$\begin{aligned} \left[ \begin{array}{r} 1 \\ 2 \\ 3 \end{array} \right] \bullet \left( \left[ \begin{array}{r} 5 \\ 4 \\ 1 \end{array} \right] - \left[ \begin{array}{r} 2 \\ 1 \\ 4 \end{array} \right] \right) &= \left[ \begin{array}{r} 1 \\ 2 \\ 3 \end{array} \right] \bullet \left( \left[ \begin{array}{r} 3 \\ 3 \\ -3 \end{array} \right] \right) \\ &= 3 + 6 - 9 = 0. \end{aligned}$$ 因此 $P = ( 5, 4, 1)$ 位于这个平面内。

Suppose $\vec{n} = \left[ \begin{array}{c} a \\ b \\ c \end{array} \right]$, $P = \left( x,y,z\right)$ and $P_0 = (x_0, y_0, z_0 )$. Then $$\begin{aligned} \vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) &= 0 \\ \left[ \begin{array}{c} a \\ b \\ c \end{array} \right] \bullet \left( \left[ \begin{array}{c} x \\ y \\ z \end{array} \right] - \left[ \begin{array}{c} x_0 \\ y_0 \\ z_0 \end{array} \right] \right) &= 0 \\ \left[ \begin{array}{c} a \\ b \\ c \end{array} \right] \bullet \left[ \begin{array}{c} x - x_0 \\ y - y_0 \\ z - z_0 \end{array} \right] &= 0 \\ a(x - x_0) + b (y - y_0) + c (z-z_0) &= 0. \end{aligned}$$ We can also write this equation as $ax + by + cz = ax_0 + by_0 + cz_0$. Notice that since $P_0$ is given, $ax_0+by_0+cz_0$ is a known scalar, which we can call $d$. This equation becomes $ax + by + cz = d$.

假设 $\vec{n} = \left[ \begin{array}{c} a \\ b \\ c \end{array} \right]$,$P = \left( x,y,z\right)$,$P_0 = (x_0, y_0, z_0 )$。则 $$\begin{aligned} \vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) &= 0 \\ \left[ \begin{array}{c} a \\ b \\ c \end{array} \right] \bullet \left( \left[ \begin{array}{c} x \\ y \\ z \end{array} \right] - \left[ \begin{array}{c} x_0 \\ y_0 \\ z_0 \end{array} \right] \right) &= 0 \\ \left[ \begin{array}{c} a \\ b \\ c \end{array} \right] \bullet \left[ \begin{array}{c} x - x_0 \\ y - y_0 \\ z - z_0 \end{array} \right] &= 0 \\ a(x - x_0) + b (y - y_0) + c (z-z_0) &= 0. \end{aligned}$$ 我们也可以把这个方程写成 $ax + by + cz = ax_0 + by_0 + cz_0$。注意:由于 $P_0$ 已知,$ax_0+by_0+cz_0$ 是一个已知的标量,我们可以把它记为 $d$。这个方程就变为 $ax + by + cz = d$。

定义 3:平面的数量方程 Scalar Equation of a Plane

Let $\vec{n} = \left[ \begin{array}{c} a \\ b \\ c \end{array} \right]$ be the normal vector for a plane which contains the point $P_0 = (x_0, y_0, z_0)$. Then if $P=(x,y,z)$ is an arbitrary point on the plane, the scalar equation of the plane is given by $ax + by + cz = d$, where $a,b,c,d \in \mathbb{R}$ and $d = ax_0 + by_0 + cz_0$.

设 $\vec{n} = \left[ \begin{array}{c} a \\ b \\ c \end{array} \right]$ 是包含点 $P_0 = (x_0, y_0, z_0)$ 的某个平面的法向量。则若 $P=(x,y,z)$ 是该平面上的任意一点,平面的数量方程(scalar equation)由 $ax + by + cz = d$ 给出,其中 $a,b,c,d \in \mathbb{R}$,且 $d = ax_0 + by_0 + cz_0$。

Consider the following equation.

请看下面的等式。

例 2:求平面的方程 Finding the Equation of a Plane

Find an equation of the plane containing $P_0 = (3, -2, 5)$ and orthogonal to $\vec{n} = \left[ \begin{array}{r} -2 \\ 4 \\ 1 \end{array} \right]$.

求包含 $P_0 = (3, -2, 5)$ 且垂直于 $\vec{n} = \left[ \begin{array}{r} -2 \\ 4 \\ 1 \end{array} \right]$ 的平面的方程。

Solution

The above vector $\vec{n}$ is the normal vector for this plane. Using Definition 2, we can determine the vector equation for this plane. $$\begin{aligned} \vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) &= 0 \\ \left[ \begin{array}{r} -2 \\ 4 \\ 1 \end{array} \right] \bullet \left(\left[ \begin{array}{c} x \\ y \\ z \end{array} \right] - \left[ \begin{array}{r} 3 \\ -2 \\ 5 \end{array} \right] \right) &= 0 \\ \left[ \begin{array}{r} -2 \\ 4 \\ 1 \end{array} \right] \bullet \left[ \begin{array}{c} x - 3 \\ y + 2 \\ z - 5 \end{array} \right] &= 0. \end{aligned}$$ Using Definition 3, we can determine the scalar equation of the plane. $-2x + 4y + 1z = -2(3) + 4(-2) + 1(5) = -9$.

上面的向量 $\vec{n}$ 就是这个平面的法向量。利用定义 2,我们可以求出这个平面的向量方程: $$\begin{aligned} \vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) &= 0 \\ \left[ \begin{array}{r} -2 \\ 4 \\ 1 \end{array} \right] \bullet \left(\left[ \begin{array}{c} x \\ y \\ z \end{array} \right] - \left[ \begin{array}{r} 3 \\ -2 \\ 5 \end{array} \right] \right) &= 0 \\ \left[ \begin{array}{r} -2 \\ 4 \\ 1 \end{array} \right] \bullet \left[ \begin{array}{c} x - 3 \\ y + 2 \\ z - 5 \end{array} \right] &= 0. \end{aligned}$$ 利用定义 3,我们可以求出平面的数量方程:$-2x + 4y + 1z = -2(3) + 4(-2) + 1(5) = -9$。

Hence, the vector equation of the plane is $\left[ \begin{array}{r} -2 \\ 4 \\ 1 \end{array} \right] \bullet \left[ \begin{array}{c} x - 3 \\ y + 2 \\ z - 5 \end{array} \right] = 0$, and the scalar equation is $-2x + 4y + 1z = -9$.

因此,这个平面的向量方程是 $\left[ \begin{array}{r} -2 \\ 4 \\ 1 \end{array} \right] \bullet \left[ \begin{array}{c} x - 3 \\ y + 2 \\ z - 5 \end{array} \right] = 0$,数量方程是 $-2x + 4y + 1z = -9$。

Suppose a point $P$ is not contained in a given plane. We are then interested in the shortest distance from that point $P$ to the given plane. Consider the following example.

假设一个点 $P$ 不在给定的平面内。那么我们关心的是从点 $P$ 到给定平面的最短距离。请看下面的例子。

例 3:从一点到一个平面的最短距离 Shortest Distance From a Point to a Plane

Find the shortest distance from the point $P = (3,2,3)$ to the plane given by $2x + y + 2z = 2$, and find the point $Q$ on the plane that is closest to $P$.

求从点 $P = (3,2,3)$ 到方程 $2x + y + 2z = 2$ 所给平面的最短距离,并求该平面上离 $P$ 最近的点 $Q$。

Solution

Pick an arbitrary point $P_0$ on the plane. Then, it follows that $\overrightarrow{QP} = \mathrm{proj}_{\vec{n}}\overrightarrow{P_0P}$ and $\| \overrightarrow{QP} \|$ is the shortest distance from $P$ to the plane. Further, the vector $\overrightarrow{0Q} = \overrightarrow{0P} - \overrightarrow{QP}$ gives the necessary point $Q$.

在平面上任取一点 $P_0$。则 $\overrightarrow{QP} = \mathrm{proj}_{\vec{n}}\overrightarrow{P_0P}$,且 $\| \overrightarrow{QP} \|$ 就是从 $P$ 到该平面的最短距离。进一步,向量 $\overrightarrow{0Q} = \overrightarrow{0P} - \overrightarrow{QP}$ 给出了所需的点 $Q$。

From the above scalar equation, we have that $\vec{n} = \left[ \begin{array}{c} 2 \\ 1 \\ 2 \end{array} \right]$. Now, choose $P_0 = (1, 0, 0)$ so that $\vec{n} \bullet \overrightarrow{0P_0} = 2 = d$. Then, $\overrightarrow{P_0P} = \left[ \begin{array}{c} 3 \\ 2 \\ 3 \end{array} \right] - \left[ \begin{array}{c} 1 \\ 0 \\ 0 \end{array} \right] = \left[ \begin{array}{c} 2 \\ 2 \\ 3 \end{array} \right]$.

由上述数量方程,我们有 $\vec{n} = \left[ \begin{array}{c} 2 \\ 1 \\ 2 \end{array} \right]$。现在取 $P_0 = (1, 0, 0)$,使得 $\vec{n} \bullet \overrightarrow{0P_0} = 2 = d$。于是 $\overrightarrow{P_0P} = \left[ \begin{array}{c} 3 \\ 2 \\ 3 \end{array} \right] - \left[ \begin{array}{c} 1 \\ 0 \\ 0 \end{array} \right] = \left[ \begin{array}{c} 2 \\ 2 \\ 3 \end{array} \right]$。

Next, compute $\overrightarrow{QP} = \mathrm{proj}_{\vec{n}}\overrightarrow{P_0P}$. $$\begin{aligned} \overrightarrow{QP} &= \mathrm{proj}_{\vec{n}}\overrightarrow{P_0P} \\ &= \left( \frac{ \overrightarrow{P_0P} \bullet \vec{n}}{\| \vec{n} \| ^2}\right)\vec{n} \\ &= \frac{12}{9} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right] \\ &= \frac{4}{3} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right]. \end{aligned}$$ Then, $\| \overrightarrow{QP} \| = 4$, so the shortest distance from $P$ to the plane is $4$.

接下来计算 $\overrightarrow{QP} = \mathrm{proj}_{\vec{n}}\overrightarrow{P_0P}$。 $$\begin{aligned} \overrightarrow{QP} &= \mathrm{proj}_{\vec{n}}\overrightarrow{P_0P} \\ &= \left( \frac{ \overrightarrow{P_0P} \bullet \vec{n}}{\| \vec{n} \| ^2}\right)\vec{n} \\ &= \frac{12}{9} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right] \\ &= \frac{4}{3} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right]. \end{aligned}$$ 于是 $\| \overrightarrow{QP} \| = 4$,所以从 $P$ 到该平面的最短距离是 $4$。

Next, to find the point $Q$ on the plane which is closest to $P$ we have $$\begin{aligned} \overrightarrow{0Q} &= \overrightarrow{0P} - \overrightarrow{QP} \\ &= \left[ \begin{array}{r} 3 \\ 2 \\ 3 \end{array} \right] - \frac{4}{3} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right] \\ &= \frac{1}{3} \left[ \begin{array}{r} 1 \\ 2 \\ 1 \end{array} \right]. \end{aligned}$$ Therefore, $Q = (\frac{1}{3}, \frac{2}{3}, \frac{1}{3} )$.

接下来,为求平面上离 $P$ 最近的点 $Q$,我们有 $$\begin{aligned} \overrightarrow{0Q} &= \overrightarrow{0P} - \overrightarrow{QP} \\ &= \left[ \begin{array}{r} 3 \\ 2 \\ 3 \end{array} \right] - \frac{4}{3} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right] \\ &= \frac{1}{3} \left[ \begin{array}{r} 1 \\ 2 \\ 1 \end{array} \right]. \end{aligned}$$ 因此,$Q = (\frac{1}{3}, \frac{2}{3}, \frac{1}{3} )$。

叉积 The Cross Product

学习目标 Outcomes

  1. Compute the cross product and box product of vectors in $\mathbb{R}^3$.
  1. 计算 $\mathbb{R}^3$ 中向量的叉积与混合积(box product)。

Recall that the dot product is one of two important products for vectors. The second type of product for vectors is called the cross product. It is important to note that the cross product is only defined in $\mathbb{R}^{3}.$ First we discuss the geometric meaning and then a description in terms of coordinates is given, both of which are important. The geometric description is essential in order to understand the applications to physics and geometry while the coordinate description is necessary to compute the cross product.

回想一下:点积是向量的两个重要乘积之一。向量的第二种乘积称为叉积(cross product)。需要强调指出,叉积仅在 $\mathbb{R}^{3}$ 中定义。我们先讨论它的几何含义,然后给出坐标描述——两者都很重要。要理解在物理和几何中的应用,几何描述必不可少;而要具体计算叉积,坐标描述则是必需的。

Consider the following definition.

请看下面的定义。

定义 1:向量的右手系 Right Hand System of Vectors

Three vectors, $\vec{u},\vec{v},\vec{w}$ form a right hand system if when you extend the fingers of your right hand along the direction of vector $\vec{u}$ and close them in the direction of $\vec{v}$, the thumb points roughly in the direction of $\vec{w}$.

三个向量 $\vec{u},\vec{v},\vec{w}$ 构成一个右手系(right hand system),是指当你把右手的手指沿向量 $\vec{u}$ 的方向伸出、再顺着 $\vec{v}$ 的方向收拢手指时,大拇指大致指向 $\vec{w}$ 的方向。

For an example of a right handed system of vectors, see the following picture.

关于一个向量右手系的例子,请看下面这幅图。
三个向量的三维图:u 指向左,v 指向屏幕外朝向观察者,w 指向上
Figure 17 图 17

In this picture the vector $\vec{w}$ points upwards from the plane determined by the other two vectors. Point the fingers of your right hand along $\vec{u}$, and close them in the direction of $\vec{v}$. Notice that if you extend the thumb on your right hand, it points in the direction of $\vec{w}$.

在这幅图中,向量 $\vec{w}$ 从另外两个向量所在的平面指向上方。把你的右手手指沿 $\vec{u}$ 的方向伸出,并顺着 $\vec{v}$ 的方向收拢。注意:如果你伸出右手的大拇指,它会指向 $\vec{w}$ 的方向。

You should consider how a right hand system would differ from a left hand system. Try using your left hand and you will see that the vector $\vec{w}$ would need to point in the opposite direction.

你应当考虑一下右手系与左手系有什么不同。试着用你的左手,你会发现向量 $\vec{w}$ 将需要指向相反的方向。

Notice that the special vectors, $\vec{i},\vec{j},\vec{k}$ will always form a right handed system. If you extend the fingers of your right hand along $\vec{i}$ and close them in the direction $\vec{j}$, the thumb points in the direction of $\vec{k}$.

注意:特殊的向量 $\vec{i},\vec{j},\vec{k}$ 总是构成一个右手系。如果你把右手的手指沿 $\vec{i}$ 方向伸出、顺着 $\vec{j}$ 的方向收拢,大拇指就指向 $\vec{k}$ 的方向。
三个向量的三维图:i 指向屏幕外,j 指向右,k 指向上
Figure 18 图 18

The following is the geometric description of the cross product. Recall that the dot product of two vectors results in a scalar. In contrast, the cross product results in a vector, as the product gives a direction as well as magnitude.

下面就是叉积的几何描述。回想一下:两个向量的点积结果是标量。与此相反,叉积的结果是一个向量,因为该乘积既给出方向又给出大小。

定义 2:叉积的几何定义 Geometric Definition of Cross Product

Let $\vec{u}$ and $\vec{v}$ be two vectors in $\mathbb{R}^{3}.$ Then the cross product, written $\vec{u}\times \vec{v}$, is defined by the following two rules.

设 $\vec{u}$ 和 $\vec{v}$ 是 $\mathbb{R}^{3}$ 中的两个向量。则叉积(cross product),记作 $\vec{u}\times \vec{v}$,由下面两条规则定义。
  1. Its length is $\| \vec{u}\times \vec{v}\| =\| \vec{u}\| \| \vec{v}\| \sin \theta,$ where $\theta$ is the included angle between $\vec{u}$ and $\vec{v}$.
  2. It is perpendicular to both $\vec{u}$ and $\vec{v}$, that is $\left( \vec{u}\times \vec{v} \right) \cdot \vec{u}=0, \left( \vec{u}\times \vec{v} \right) \cdot \vec{v}=0,$ and $\vec{u},\vec{v},\vec{u}\times \vec{v}$ form a right hand system.
  1. 它的长度是 $\| \vec{u}\times \vec{v}\| =\| \vec{u}\| \| \vec{v}\| \sin \theta,$,其中 $\theta$ 是 $\vec{u}$ 与 $\vec{v}$ 之间的夹角。
  2. 它与 $\vec{u}$ 和 $\vec{v}$ 都垂直,即 $\left( \vec{u}\times \vec{v} \right) \cdot \vec{u}=0, \left( \vec{u}\times \vec{v} \right) \cdot \vec{v}=0,$ 且 $\vec{u},\vec{v},\vec{u}\times \vec{v}$ 构成右手系。

The cross product of the special vectors $\vec{i}, \vec{j}, \vec{k}$ is as follows. $$\begin{array}{cc} \vec{i}\times \vec{j}=\vec{k} & \vec{j}\times \vec{i}=-\vec{k} \\ \vec{k}\times \vec{i}=\vec{j} & \vec{i}\times \vec{k}=-\vec{j} \\ \vec{j}\times \vec{k}=\vec{i} & \vec{k}\times \vec{j}=-\vec{i} \end{array}$$ With this information, the following gives the coordinate description of the cross product.

特殊向量 $\vec{i}, \vec{j}, \vec{k}$ 之间的叉积如下: $$\begin{array}{cc} \vec{i}\times \vec{j}=\vec{k} & \vec{j}\times \vec{i}=-\vec{k} \\ \vec{k}\times \vec{i}=\vec{j} & \vec{i}\times \vec{k}=-\vec{j} \\ \vec{j}\times \vec{k}=\vec{i} & \vec{k}\times \vec{j}=-\vec{i} \end{array}$$ 有了这些信息,下面给出叉积的坐标描述。

Recall that the vector $\vec{u}= \left[ \begin{array}{ccc} u_1 & u_2 & u_3 \end{array} \right]^T$ can be written in terms of $\vec{i}, \vec{j}, \vec{k}$ as $\vec{u}=u_{1}\vec{i}+u_{2}\vec{j}+u_{3}\vec{k}$.

回想一下:向量 $\vec{u}= \left[ \begin{array}{ccc} u_1 & u_2 & u_3 \end{array} \right]^T$ 可以用 $\vec{i}, \vec{j}, \vec{k}$ 写成 $\vec{u}=u_{1}\vec{i}+u_{2}\vec{j}+u_{3}\vec{k}$。

定理 1:叉积的坐标描述 Coordinate Description of Cross Product

Let $\vec{u}=u_{1}\vec{i}+u_{2}\vec{j}+u_{3}\vec{k}$ and $\vec{v}=v_{1}\vec{i}+v_{2}\vec{j}+v_{3}\vec{k}$ be two vectors. Then $$\begin{array}{c} \vec{u}\times \vec{v} =\left( u_{2}v_{3}-u_{3}v_{2}\right) \vec{i}-\left( u_{1}v_{3} - u_{3}v_{1}\right) \vec{j}+ \left( u_{1}v_{2}-u_{2}v_{1}\right) \vec{k} \end{array}$$ Writing $\vec{u} \times \vec{v}$ in the usual way, it is given by $$\vec{u} \times \vec{v} = \left[ \begin{array}{r} u_{2}v_{3}-u_{3}v_{2} \\ -(u_{1}v_{3}-u_{3}v_{1}) \\ u_{1}v_{2}-u_{2}v_{1} \end{array} \right].$$

设 $\vec{u}=u_{1}\vec{i}+u_{2}\vec{j}+u_{3}\vec{k}$ 和 $\vec{v}=v_{1}\vec{i}+v_{2}\vec{j}+v_{3}\vec{k}$ 是两个向量。则 $$\begin{array}{c} \vec{u}\times \vec{v} =\left( u_{2}v_{3}-u_{3}v_{2}\right) \vec{i}-\left( u_{1}v_{3} - u_{3}v_{1}\right) \vec{j}+ \left( u_{1}v_{2}-u_{2}v_{1}\right) \vec{k} \end{array}$$ 按通常的方式写出 $\vec{u} \times \vec{v}$,它由下式给出 $$\vec{u} \times \vec{v} = \left[ \begin{array}{r} u_{2}v_{3}-u_{3}v_{2} \\ -(u_{1}v_{3}-u_{3}v_{1}) \\ u_{1}v_{2}-u_{2}v_{1} \end{array} \right].$$

We now prove this proposition.

下面我们来证明这个命题。

证明 Proof

From the above table and the properties of the cross product listed, $$\begin{aligned} \vec{u} \times \vec{v} &= \left( u_{1}\vec{i}+u_{2}\vec{j}+u_{3}\vec{k}\right) \times \left( v_{1}\vec{i}+v_{2}\vec{j}+v_{3}\vec{k}\right) \\ &= u_{1}v_{2}\vec{i}\times \vec{j}+u_{1}v_{3}\vec{i}\times \vec{k}+u_{2}v_{1}\vec{j}\times \vec{i}+ u_{2}v_{3}\vec{j}\times \vec{k}+ +u_{3}v_{1}\vec{k}\times \vec{i}+u_{3}v_{2}\vec{k}\times \vec{j} \\ &=u_{1}v_{2}\vec{k}-u_{1}v_{3}\vec{j}-u_{2}v_{1}\vec{k}+u_{2}v_{3} \vec{i}+u_{3}v_{1}\vec{j}-u_{3}v_{2}\vec{i} \\ &=\left( u_{2}v_{3}-u_{3}v_{2}\right) \vec{i}+\left( u_{3}v_{1}-u_{1}v_{3}\right) \vec{j}+\left( u_{1}v_{2}-u_{2}v_{1}\right) \vec{k} \end{aligned}$$

由上面这张表以及所列出的叉积性质, $$\begin{aligned} \vec{u} \times \vec{v} &= \left( u_{1}\vec{i}+u_{2}\vec{j}+u_{3}\vec{k}\right) \times \left( v_{1}\vec{i}+v_{2}\vec{j}+v_{3}\vec{k}\right) \\ &= u_{1}v_{2}\vec{i}\times \vec{j}+u_{1}v_{3}\vec{i}\times \vec{k}+u_{2}v_{1}\vec{j}\times \vec{i}+ u_{2}v_{3}\vec{j}\times \vec{k}+ +u_{3}v_{1}\vec{k}\times \vec{i}+u_{3}v_{2}\vec{k}\times \vec{j} \\ &=u_{1}v_{2}\vec{k}-u_{1}v_{3}\vec{j}-u_{2}v_{1}\vec{k}+u_{2}v_{3} \vec{i}+u_{3}v_{1}\vec{j}-u_{3}v_{2}\vec{i} \\ &=\left( u_{2}v_{3}-u_{3}v_{2}\right) \vec{i}+\left( u_{3}v_{1}-u_{1}v_{3}\right) \vec{j}+\left( u_{1}v_{2}-u_{2}v_{1}\right) \vec{k} \end{aligned}$$

There is another version of the coordinate formula which may be easier to remember. We can express the cross product as the determinant of a matrix, as follows. $$\vec{u}\times \vec{v} = \left\vert \begin{array}{ccc} \vec{i} & \vec{j} & \vec{k} \\ u_{1} & u_{2} & u_{3} \\ v_{1} & v_{2} & v_{3} \end{array} \right\vert.$$ Expanding the determinant along the top row yields $$\vec{i}\left( -1\right) ^{1+1}\left\vert \begin{array}{cc} u_{2} & u_{3} \\ v_{2} & v_{3} \end{array} \right\vert +\vec{j}\left( -1\right) ^{2+1}\left\vert \begin{array}{cc} u_{1} & u_{3} \\ v_{1} & v_{3} \end{array} \right\vert +\vec{k}\left( -1\right) ^{3+1}\left\vert \begin{array}{cc} u_{1} & u_{2} \\ v_{1} & v_{2} \end{array} \right\vert$$ $$=\vec{i}\left\vert \begin{array}{cc} u_{2} & u_{3} \\ v_{2} & v_{3} \end{array} \right\vert -\vec{j}\left\vert \begin{array}{cc} u_{1} & u_{3} \\ v_{1} & v_{3} \end{array} \right\vert +\vec{k}\left\vert \begin{array}{cc} u_{1} & u_{2} \\ v_{1} & v_{2} \end{array} \right\vert.$$ Expanding these determinants leads to $\left( u_{2}v_{3}-u_{3}v_{2}\right) \vec{i}-\left( u_{1}v_{3}-u_{3}v_{1}\right) \vec{j}+\left( u_{1}v_{2}-u_{2}v_{1}\right) \vec{k}$, which is the same as above.

坐标公式还有另一个可能更容易记住的版本。我们可以把叉积表示成某个矩阵的行列式,如下: $$\vec{u}\times \vec{v} = \left\vert \begin{array}{ccc} \vec{i} & \vec{j} & \vec{k} \\ u_{1} & u_{2} & u_{3} \\ v_{1} & v_{2} & v_{3} \end{array} \right\vert.$$ 沿第一行展开这个行列式,得 $$\vec{i}\left( -1\right) ^{1+1}\left\vert \begin{array}{cc} u_{2} & u_{3} \\ v_{2} & v_{3} \end{array} \right\vert +\vec{j}\left( -1\right) ^{2+1}\left\vert \begin{array}{cc} u_{1} & u_{3} \\ v_{1} & v_{3} \end{array} \right\vert +\vec{k}\left( -1\right) ^{3+1}\left\vert \begin{array}{cc} u_{1} & u_{2} \\ v_{1} & v_{2} \end{array} \right\vert$$ $$=\vec{i}\left\vert \begin{array}{cc} u_{2} & u_{3} \\ v_{2} & v_{3} \end{array} \right\vert -\vec{j}\left\vert \begin{array}{cc} u_{1} & u_{3} \\ v_{1} & v_{3} \end{array} \right\vert +\vec{k}\left\vert \begin{array}{cc} u_{1} & u_{2} \\ v_{1} & v_{2} \end{array} \right\vert.$$ 展开这些行列式就得到 $\left( u_{2}v_{3}-u_{3}v_{2}\right) \vec{i}-\left( u_{1}v_{3}-u_{3}v_{1}\right) \vec{j}+\left( u_{1}v_{2}-u_{2}v_{1}\right) \vec{k}$,与上面的公式相同。

The cross product satisfies the following properties.

叉积满足下面这些性质。

命题 1:叉积的性质 Properties of the Cross Product

Let $\vec{u}, \vec{v}, \vec{w}$ be vectors in $\mathbb{R}^3$, and $k$ a scalar. Then, the following properties of the cross product hold.

设 $\vec{u}, \vec{v}, \vec{w}$ 是 $\mathbb{R}^3$ 中的向量,$k$ 是标量。则叉积的下列性质成立。
  1. $\vec{u}\times \vec{v}= -\left( \vec{v}\times \vec{u}\right), \mbox{ and } \vec{u}\times \vec{u}=\vec{0}$
  2. $\left( k \vec{u}\right)\times \vec{v}= k \left( \vec{u}\times \vec{v}\right) =\vec{u}\times \left( k \vec{v}\right)$
  3. $\vec{u}\times \left( \vec{v}+\vec{w}\right) =\vec{u}\times \vec{v}+\vec{u}\times \vec{w}$
  4. $\left( \vec{v}+\vec{w}\right) \times \vec{u}=\vec{v} \times \vec{u}+\vec{w}\times \vec{u}$
  1. $\vec{u}\times \vec{v}= -\left( \vec{v}\times \vec{u}\right), \quad \text{且 } \vec{u}\times \vec{u}=\vec{0}$
  2. $\left( k \vec{u}\right)\times \vec{v}= k \left( \vec{u}\times \vec{v}\right) =\vec{u}\times \left( k \vec{v}\right)$
  3. $\vec{u}\times \left( \vec{v}+\vec{w}\right) =\vec{u}\times \vec{v}+\vec{u}\times \vec{w}$
  4. $\left( \vec{v}+\vec{w}\right) \times \vec{u}=\vec{v} \times \vec{u}+\vec{w}\times \vec{u}$

Proof. Formula $1.$ follows immediately from the definition. The vectors $\vec{u}\times \vec{v}$ and $\vec{v}\times \vec{u}$ have the same magnitude, $\left\vert \vec{u}\right\vert \left\vert \vec{v}\right\vert \sin \theta ,$ and an application of the right hand rule shows they have opposite direction.

证明。公式 $1.$ 直接从定义得出。向量 $\vec{u}\times \vec{v}$ 和 $\vec{v}\times \vec{u}$ 具有相同的大小 $\left\vert \vec{u}\right\vert \left\vert \vec{v}\right\vert \sin \theta ,$,而应用右手定则可以表明它们的方向相反。

Formula $2.$ is proven as follows. If $k$ is a non-negative scalar, the direction of $\left( k \vec{u}\right) \times \vec{v}$ is the same as the direction of $\vec{u}\times \vec{v}, k \left( \vec{u}\times \vec{v}\right)$ and $\vec{u}\times \left( k \vec{v}\right)$. The magnitude is $k$ times the magnitude of $\vec{u}\times \vec{v}$ which is the same as the magnitude of $k \left( \vec{u}\times \vec{v}\right)$ and $\vec{u}\times \left( k \vec{v}\right) .$ Using this yields equality in $2$. In the case where $k <0,$ everything works the same way except the vectors are all pointing in the opposite direction and you must multiply by $\left\vert k \right\vert$ when comparing their magnitudes.

公式 $2.$ 的证明如下。若 $k$ 是非负标量,则 $\left( k \vec{u}\right) \times \vec{v}$ 的方向与 $\vec{u}\times \vec{v}$、$k \left( \vec{u}\times \vec{v}\right)$ 以及 $\vec{u}\times \left( k \vec{v}\right)$ 的方向相同;其大小为 $\vec{u}\times \vec{v}$ 大小的 $k$ 倍,这与 $k \left( \vec{u}\times \vec{v}\right)$ 和 $\vec{u}\times \left( k \vec{v}\right) .$ 的大小相同。利用这一点就得到 $2$ 中的等式。当 $k <0,$ 时,一切同理,只是各向量都指向相反方向,比较大小时要乘以 $\left\vert k \right\vert$。

The distributive laws, $3.$ and $4.$, are much harder to establish. For now, it suffices to notice that if we know that $3.$ is true, $4.$ follows. Thus, assuming $3.$, and using $1.$, $$\begin{aligned} \left( \vec{v}+\vec{w}\right) \times \vec{u}& =-\vec{u}\times \left( \vec{v}+\vec{w}\right) \\ & =-\left( \vec{u}\times \vec{v}+\vec{u}\times \vec{w}\right) \\ & =\vec{v}\times \vec{u}+\vec{w}\times \vec{u}. \end{aligned}$$

分配律 $3.$ 和 $4.$ 更难建立。就现在而言,只须注意到:若 $3.$ 成立,则 $4.$ 随之成立。于是,假设 $3.$ 成立,并利用 $1.$, $$\begin{aligned} \left( \vec{v}+\vec{w}\right) \times \vec{u}& =-\vec{u}\times \left( \vec{v}+\vec{w}\right) \\ & =-\left( \vec{u}\times \vec{v}+\vec{u}\times \vec{w}\right) \\ & =\vec{v}\times \vec{u}+\vec{w}\times \vec{u}. \end{aligned}$$

We will now look at an example of how to compute a cross product.

现在来看一个如何计算叉积的例子。

例 1:求一个叉积 Find a Cross Product

Find $\vec{u} \times \vec{v}$ for the following vectors $$\vec{u} = \left[ \begin{array}{r} 1 \\ -1 \\ 2 \end{array} \right], \vec{v} = \left[ \begin{array}{r} 3 \\ -2 \\ 1 \end{array} \right].$$

对下面的向量求 $\vec{u} \times \vec{v}$: $$\vec{u} = \left[ \begin{array}{r} 1 \\ -1 \\ 2 \end{array} \right], \vec{v} = \left[ \begin{array}{r} 3 \\ -2 \\ 1 \end{array} \right].$$

Solution

Note that we can write $\vec{u}, \vec{v}$ in terms of the special vectors $\vec{i}, \vec{j}, \vec{k}$ as $$\begin{array}{c} \vec{u} = \vec{i}-\vec{j}+2\vec{k} \\ \vec{v} = 3\vec{i}-2\vec{j}+\vec{k}. \end{array}$$ We will use the determinant formula to compute the cross product. $$\vec{u} \times \vec{v} = \left\vert \begin{array}{rrr} \vec{i} & \vec{j} & \vec{k} \\ 1 & -1 & 2 \\ 3 & -2 & 1 \end{array} \right\vert =\left\vert \begin{array}{rr} -1 & 2 \\ -2 & 1 \end{array} \right\vert \vec{i}-\left\vert \begin{array}{rr} 1 & 2 \\ 3 & 1 \end{array} \right\vert \vec{j}+\left\vert \begin{array}{rr} 1 & -1 \\ 3 & -2 \end{array} \right\vert \vec{k}=3\vec{i}+5\vec{j}+\vec{k}.$$ We can write this result in the usual way, as $\vec{u} \times \vec{v} = \left[ \begin{array}{r} 3 \\ 5 \\ 1 \end{array} \right]$.

注意:我们可以把 $\vec{u}, \vec{v}$ 用特殊向量 $\vec{i}, \vec{j}, \vec{k}$ 写成 $$\begin{array}{c} \vec{u} = \vec{i}-\vec{j}+2\vec{k} \\ \vec{v} = 3\vec{i}-2\vec{j}+\vec{k}. \end{array}$$ 我们用行列式公式来计算叉积: $$\vec{u} \times \vec{v} = \left\vert \begin{array}{rrr} \vec{i} & \vec{j} & \vec{k} \\ 1 & -1 & 2 \\ 3 & -2 & 1 \end{array} \right\vert =\left\vert \begin{array}{rr} -1 & 2 \\ -2 & 1 \end{array} \right\vert \vec{i}-\left\vert \begin{array}{rr} 1 & 2 \\ 3 & 1 \end{array} \right\vert \vec{j}+\left\vert \begin{array}{rr} 1 & -1 \\ 3 & -2 \end{array} \right\vert \vec{k}=3\vec{i}+5\vec{j}+\vec{k}.$$ 我们可以把这个结果按通常方式写成 $\vec{u} \times \vec{v} = \left[ \begin{array}{r} 3 \\ 5 \\ 1 \end{array} \right]$。

An important geometrical application of the cross product is as follows. The size of the cross product, $\| \vec{u}\times \vec{v}\|$, is the area of the parallelogram determined by $\vec{u}$ and $\vec{v}$, as shown in the following picture.

叉积的一个重要的几何应用如下:叉积的大小 $\| \vec{u}\times \vec{v}\|$ 就是由 $\vec{u}$ 和 $\vec{v}$ 确定的平行四边形的面积,如下面这幅图所示。
向量 u 和 v 作为平行四边形的两条边,向量之间的夹角标为 theta。平行四边形的高,从 v 的尖端垂直落到 u 上,标为 v 的大小乘正弦 theta。
Figure 19 图 19

We examine this concept in the following example.

在下面的例子中我们考察这一概念。

例 2:平行四边形的面积 Area of a Parallelogram

Find the area of the parallelogram determined by the vectors $\vec{u}$ and $\vec{v}$ given by $$\vec{u} = \left[ \begin{array}{r} 1 \\ -1 \\ 2 \end{array} \right], \vec{v} = \left[ \begin{array}{r} 3 \\ -2 \\ 1 \end{array} \right].$$

求由向量 $\vec{u}$ 和 $\vec{v}$ 确定的平行四边形的面积: $$\vec{u} = \left[ \begin{array}{r} 1 \\ -1 \\ 2 \end{array} \right], \vec{v} = \left[ \begin{array}{r} 3 \\ -2 \\ 1 \end{array} \right].$$

Solution

Notice that these vectors are the same as the ones given in Example 1. Recall from the geometric description of the cross product, that the area of the parallelogram is simply the magnitude of $\vec{u} \times \vec{v}$. From Example 1, $\vec{u} \times \vec{v} = 3\vec{i}+5\vec{j}+\vec{k}$. We can also write this as $$\vec{u} \times \vec{v} = \left[ \begin{array}{r} 3 \\ 5 \\ 1 \end{array} \right].$$ Thus the area of the parallelogram is $$\| \vec{u} \times \vec{v} \| = \sqrt{(3)(3) + (5)(5) + (1)(1)} = \sqrt{9+25+1}=\sqrt{35}.$$

注意:这些向量与例 1 中给出的相同。回想叉积的几何描述,平行四边形的面积就是 $\vec{u} \times \vec{v}$ 的大小。由例 1,$\vec{u} \times \vec{v} = 3\vec{i}+5\vec{j}+\vec{k}$。我们也可以把它写成 $$\vec{u} \times \vec{v} = \left[ \begin{array}{r} 3 \\ 5 \\ 1 \end{array} \right].$$ 因此,这个平行四边形的面积是 $$\| \vec{u} \times \vec{v} \| = \sqrt{(3)(3) + (5)(5) + (1)(1)} = \sqrt{9+25+1}=\sqrt{35}.$$

We can also use this concept to find the area of a triangle. Consider the following example.

我们也可以用这一概念来求三角形的面积。请看下面的例子。

例 3:三角形的面积 Area of Triangle

Find the area of the triangle determined by the points $\left(1, 2, 3 \right) , \left( 0,2,5\right), \left( 5,1, 2 \right)$.

求由点 $\left(1, 2, 3 \right) , \left( 0,2,5\right), \left( 5,1, 2 \right)$ 确定的三角形的面积。

Solution

This triangle is obtained by connecting the three points with lines. Picking $\left( 1,2,3\right)$ as a starting point, there are two displacement vectors, $\left[ \begin{array}{rrr} -1 & 0 & 2 \end{array} \right]^T$ and $\left[ \begin{array}{rrr} 4 & -1 & -1 \end{array} \right]^T$. Notice that if we add either of these vectors to the position vector of the starting point, the result is the position vectors of the other two points. Now, the area of the triangle is half the area of the parallelogram determined by $\left[ \begin{array}{rrr} -1 & 0 & 2 \end{array} \right]^T$ and $\left[ \begin{array}{rrr} 4 & -1 & -1 \end{array} \right]^T.$ The required cross product is given by $$\left[ \begin{array}{r} -1 \\ 0 \\ 2 \end{array} \right] \times \left[ \begin{array}{r} 4 \\ -1 \\ -1 \end{array} \right] = \left[ \begin{array}{rrr} 2 & 7 & 1 \end{array} \right].$$ Taking the size of this vector gives the area of the parallelogram, given by $$\sqrt{(2)(2) + (7)(7) + (1)(1)} = \sqrt{4+49+1} = \sqrt{54}.$$ Hence the area of the triangle is $\frac{1}{2}\sqrt{54}= \frac{3}{2}\sqrt{6}.$

这个三角形通过用直线连接这三个点得到。选取 $\left( 1,2,3\right)$ 作为起点,有两个位移向量:$\left[ \begin{array}{rrr} -1 & 0 & 2 \end{array} \right]^T$ 和 $\left[ \begin{array}{rrr} 4 & -1 & -1 \end{array} \right]^T$。注意:把这两个向量中的任一个加到起点的位置向量上,得到的就是另外两个点的位置向量。现在,这个三角形的面积是 $\left[ \begin{array}{rrr} -1 & 0 & 2 \end{array} \right]^T$ 和 $\left[ \begin{array}{rrr} 4 & -1 & -1 \end{array} \right]^T$ 所确定的平行四边形面积的一半。所需的叉积是 $$\left[ \begin{array}{r} -1 \\ 0 \\ 2 \end{array} \right] \times \left[ \begin{array}{r} 4 \\ -1 \\ -1 \end{array} \right] = \left[ \begin{array}{rrr} 2 & 7 & 1 \end{array} \right].$$ 取这个向量的大小,就得到平行四边形的面积: $$\sqrt{(2)(2) + (7)(7) + (1)(1)} = \sqrt{4+49+1} = \sqrt{54}.$$ 因此,这个三角形的面积是 $\frac{1}{2}\sqrt{54}= \frac{3}{2}\sqrt{6}.$

In general, if you have three points in $\mathbb{R}^{3}, P,Q,R$, the area of the triangle is given by $\frac{1}{2}\| \vec{PQ} \times \vec{PR} \|$.

一般地,如果 $\mathbb{R}^{3}$ 中有三个点 $P,Q,R$,则三角形的面积由 $\frac{1}{2}\| \vec{PQ} \times \vec{PR} \|$ 给出。

Recall that $\vec{PQ}$ is the vector running from point $P$ to point $Q$.

回想一下:$\vec{PQ}$ 是从点 $P$ 指向点 $Q$ 的向量。
一个角为 P、Q、R 的三角形。PQ 和 PR 是向量。
Figure 20 图 20

In the next section, we explore another application of the cross product.

在下一节中,我们探讨叉积的另一个应用。

混合积 The Box Product

Recall that we can use the cross product to find the area of a parallelogram. It follows that we can use the cross product together with the dot product to find the volume of a parallelepiped. We begin with a definition.

回想一下:我们可以用叉积求平行四边形的面积。由此,我们可以把叉积与点积结合起来求平行六面体的体积。我们先给出一个定义。

定义 3:平行六面体 Parallelepiped

A parallelepiped determined by the three vectors, $\vec{u},\vec{v}$, and $\vec{w}$ consists of $\left\{ r\vec{u}+s\vec{v}+t\vec{w}:r,s,t\in \left[ 0,1\right] \right\}$.

由三个向量 $\vec{u},\vec{v}$ 和 $\vec{w}$ 确定的平行六面体(parallelepiped)由 $\left\{ r\vec{u}+s\vec{v}+t\vec{w}:r,s,t\in \left[ 0,1\right] \right\}$ 构成。

That is, if you pick three numbers, $r,s,$ and $t$ each in $\left[ 0,1\right]$ and form $r\vec{u}+s\vec{v}+t\vec{w}$ then the collection of all such points makes up the parallelepiped determined by these three vectors.

也就是说,如果你选出三个数 $r,s,t$,使它们都在 $\left[ 0,1\right]$ 中,并构成 $r\vec{u}+s\vec{v}+t\vec{w}$,那么所有这些点的集合就拼成由这三个向量确定的平行六面体。

The following is an example of a parallelepiped.

下面就是一个平行六面体的例子。
一个平行六面体的三维图,三条边标为向量 u、v、w。向量 u 叉乘 v 显示为垂直于 u 和 v 形成的平面,w 与 u 叉乘 v 之间的夹角标为 theta。
Figure 21 图 21

Notice that the base of the parallelepiped is the parallelogram determined by the vectors $\vec{u}$ and $\vec{v}$. Therefore, its area is equal to $\| \vec{u}\times \vec{v} \|$. The height of the parallelepiped is $\| \vec{w}\| \cos \theta$, where $\theta$ is the angle shown in the picture between $\vec{w}$ and $\vec{u}\times \vec{v}$. The volume of this parallelepiped is the area of the base times the height, which is just $$\| \vec{u}\times \vec{v}\| \| \vec{w}\| \cos \theta = \left( \vec{u}\times\vec{v}\right) \cdot \vec{w}.$$ This expression is known as the box product and is sometimes written as $\left[ \vec{u},\vec{v},\vec{w}\right] .$ You should consider what happens if you interchange the $\vec{v}$ with the $\vec{w}$ or the $\vec{u}$ with the $\vec{w}$. You can see geometrically from drawing pictures that this merely introduces a minus sign. In any case the box product of three vectors always equals either the volume of the parallelepiped determined by the three vectors or else $-1$ times this volume.

注意:这个平行六面体的底面就是由向量 $\vec{u}$ 和 $\vec{v}$ 确定的平行四边形。因此它的面积等于 $\| \vec{u}\times \vec{v} \|$。平行六面体的高是 $\| \vec{w}\| \cos \theta$,其中 $\theta$ 是图中所示 $\vec{w}$ 与 $\vec{u}\times \vec{v}$ 之间的夹角。这个平行六面体的体积等于底面积乘以高,即 $$\| \vec{u}\times \vec{v}\| \| \vec{w}\| \cos \theta = \left( \vec{u}\times\vec{v}\right) \cdot \vec{w}.$$ 这个表达式称为混合积(box product),有时写成 $\left[ \vec{u},\vec{v},\vec{w}\right] .$ 你应当考虑:把 $\vec{v}$ 与 $\vec{w}$ 互换、或者把 $\vec{u}$ 与 $\vec{w}$ 互换时会发生什么。从画图上你可以几何地看到,这只不过会引入一个负号。无论如何,三个向量的混合积总是等于这三个向量所确定的平行六面体的体积,或者等于该体积的 $-1$ 倍。

命题 2:混合积 The Box Product

Let $\vec{u}, \vec{v}, \vec{w}$ be three vectors in $\mathbb{R}^n$ that define a parallelepiped. Then the volume of the parallelepiped is the absolute value of the box product, given by $\left\| \left(\vec{u}\times\vec{v}\right) \cdot \vec{w} \right\|$.

设 $\vec{u}, \vec{v}, \vec{w}$ 是 $\mathbb{R}^n$ 中定义了一个平行六面体的三个向量。则这个平行六面体的体积是混合积的绝对值,即 $\left\| \left(\vec{u}\times\vec{v}\right) \cdot \vec{w} \right\|$。

Consider an example of this concept.

考虑这个概念的一个例子。

例 4:平行六面体的体积 Volume of a Parallelepiped

Find the volume of the parallelepiped determined by the vectors $$\vec{u} = \left[ \begin{array}{r} 1 \\ 2 \\ -5 \end{array} \right], \vec{v} = \left[ \begin{array}{r} 1 \\ 3 \\ -6 \end{array} \right], \vec{w} = \left[ \begin{array}{r} 3 \\ 2 \\ 3 \end{array} \right].$$

求由向量 $$\vec{u} = \left[ \begin{array}{r} 1 \\ 2 \\ -5 \end{array} \right], \vec{v} = \left[ \begin{array}{r} 1 \\ 3 \\ -6 \end{array} \right], \vec{w} = \left[ \begin{array}{r} 3 \\ 2 \\ 3 \end{array} \right]$$ 确定的平行六面体的体积。

Solution

According to the above discussion, pick any two of these vectors, take the cross product and then take the dot product of this with the third of these vectors. The result will be either the desired volume or $-1$ times the desired volume. Therefore by taking the absolute value of the result, we obtain the volume.

根据上面的讨论,任选其中两个向量,求它们的叉积,再把这个叉积与剩余第三个向量作点积。结果要么是所求的体积,要么是所求体积的 $-1$ 倍。因此,对结果取绝对值,就得到体积。

We will take the cross product of $\vec{u}$ and $\vec{v}$. This is given by $$\vec{u} \times \vec{v} = \left[ \begin{array}{r} 1 \\ 2 \\ -5 \end{array} \right] \times \left[ \begin{array}{r} 1 \\ 3 \\ -6 \end{array} \right] =\left\vert \begin{array}{rrr} \vec{i} & \vec{j} & \vec{k} \\ 1 & 2 & -5 \\ 1 & 3 & -6 \end{array} \right\vert = 3\vec{i}+\vec{j}+\vec{k} = \left[ \begin{array}{r} 3 \\ 1 \\ 1 \end{array} \right].$$ Now take the dot product of this vector with $\vec{w}$, which yields $$\begin{aligned} (\vec{u} \times \vec{v}) \cdot \vec{w} &= \left[ \begin{array}{r} 3 \\ 1 \\ 1 \end{array} \right] \cdot \left[ \begin{array}{r} 3 \\ 2 \\ 3 \end{array} \right] \\ &=\left( 3\vec{i}+\vec{j}+\vec{k}\right) \cdot \left( 3\vec{i}+2\vec{j}+3\vec{k}\right) \\ &=9+2+3 \\ &=14. \end{aligned}$$ This shows the volume of this parallelepiped is 14 cubic units.

我们求 $\vec{u}$ 和 $\vec{v}$ 的叉积,它为 $$\vec{u} \times \vec{v} = \left[ \begin{array}{r} 1 \\ 2 \\ -5 \end{array} \right] \times \left[ \begin{array}{r} 1 \\ 3 \\ -6 \end{array} \right] =\left\vert \begin{array}{rrr} \vec{i} & \vec{j} & \vec{k} \\ 1 & 2 & -5 \\ 1 & 3 & -6 \end{array} \right\vert = 3\vec{i}+\vec{j}+\vec{k} = \left[ \begin{array}{r} 3 \\ 1 \\ 1 \end{array} \right].$$ 现在把这个向量与 $\vec{w}$ 作点积,得到 $$\begin{aligned} (\vec{u} \times \vec{v}) \cdot \vec{w} &= \left[ \begin{array}{r} 3 \\ 1 \\ 1 \end{array} \right] \cdot \left[ \begin{array}{r} 3 \\ 2 \\ 3 \end{array} \right] \\ &=\left( 3\vec{i}+\vec{j}+\vec{k}\right) \cdot \left( 3\vec{i}+2\vec{j}+3\vec{k}\right) \\ &=9+2+3 \\ &=14. \end{aligned}$$ 这说明这个平行六面体的体积是 14 立方单位。

There is a fundamental observation which comes directly from the geometric definitions of the cross product and the dot product.

有一个基本事实直接来自叉积和点积的几何定义。

命题 3:乘积的次序 Order of the Product

Let $\vec{u},\vec{v}$, and $\vec{w}$ be vectors. Then $\left( \vec{u}\times \vec{v}\right) \cdot \vec{w}=\vec{u}\cdot \left( \vec{v}\times \vec{w} \right) .$

设 $\vec{u},\vec{v}$ 和 $\vec{w}$ 是向量。则 $\left( \vec{u}\times \vec{v}\right) \cdot \vec{w}=\vec{u}\cdot \left( \vec{v}\times \vec{w} \right) .$

证明 Proof

This follows from observing that either $\left( \vec{u}\times \vec{v}\right) \cdot \vec{w}$ and $\vec{u}\cdot \left( \vec{v}\times \vec{w}\right)$ both give the volume of the parallelepiped or they both give $-1$ times the volume.

这由下面的事实推出:$\left( \vec{u}\times \vec{v}\right) \cdot \vec{w}$ 与 $\vec{u}\cdot \left( \vec{v}\times \vec{w}\right)$ 要么都给出平行六面体的体积,要么都给出该体积的 $-1$ 倍。

Recall that we can express the cross product as the determinant of a particular matrix. It turns out that the same can be done for the box product. Suppose you have three vectors, $\vec{u}=\left[ \begin{array}{rrr} a & b & c \end{array} \right]^T ,\vec{v}=\left[ \begin{array}{rrr} d & e & f \end{array} \right]^T ,$ and $\vec{w}=\left[ \begin{array}{rrr} g & h & i \end{array} \right]^T .$ Then the box product $\vec{u}\cdot \left(\vec{v}\times \vec{w}\right)$ is given by the following. $$\begin{aligned} \vec{u}\cdot \left(\vec{v}\times \vec{w}\right) &= \left[ \begin{array}{r} a \\ b \\ c \end{array} \right] \cdot \left\| \begin{array}{rrr} \vec{i} & \vec{j} & \vec{k} \\ d & e & f \\ g & h & i \end{array} \right\| \\ &=a\left\| \begin{array}{rr} e & f \\ h & i \end{array} \right\| -b\left\| \begin{array}{rr} d & f \\ g & i \end{array} \right\| +c\left\| \begin{array}{rr} d & e \\ g & h \end{array} \right\| \\ &= \det \left[ \begin{array}{rrr} a & b & c \\ d & e & f \\ g & h & i \end{array} \right]. \end{aligned}$$

回想一下:我们可以把叉积表示成某个矩阵的行列式。事实证明,混合积也可以这样做。假设有三个向量 $\vec{u}=\left[ \begin{array}{rrr} a & b & c \end{array} \right]^T ,\vec{v}=\left[ \begin{array}{rrr} d & e & f \end{array} \right]^T ,$ 和 $\vec{w}=\left[ \begin{array}{rrr} g & h & i \end{array} \right]^T .$ 则混合积 $\vec{u}\cdot \left(\vec{v}\times \vec{w}\right)$ 由下式给出。 $$\begin{aligned} \vec{u}\cdot \left(\vec{v}\times \vec{w}\right) &= \left[ \begin{array}{r} a \\ b \\ c \end{array} \right] \cdot \left\| \begin{array}{rrr} \vec{i} & \vec{j} & \vec{k} \\ d & e & f \\ g & h & i \end{array} \right\| \\ &=a\left\| \begin{array}{rr} e & f \\ h & i \end{array} \right\| -b\left\| \begin{array}{rr} d & f \\ g & i \end{array} \right\| +c\left\| \begin{array}{rr} d & e \\ g & h \end{array} \right\| \\ &= \det \left[ \begin{array}{rrr} a & b & c \\ d & e & f \\ g & h & i \end{array} \right]. \end{aligned}$$

To take the box product, you can simply take the determinant of the matrix which results by letting the rows be the components of the given vectors in the order in which they occur in the box product.

要求混合积,你只需取这样一个矩阵的行列式:让该矩阵的各行依次为给定向量的各分量,次序与它们在混合积中出现的次序相同。

This follows directly from the definition of the cross product given above and the way we expand determinants. Thus the volume of a parallelepiped determined by the vectors $\vec{u},\vec{v},\vec{w}$ is just the absolute value of the above determinant.

这直接由上面给出的叉积定义以及我们展开行列式的方式推出。因此,由向量 $\vec{u},\vec{v},\vec{w}$ 确定的平行六面体的体积,就是上面那个行列式的绝对值。

4.11 Gram-Schmidt 正交化过程 Gram-Schmidt Process

The Gram-Schmidt process is an algorithm to transform a set of vectors into an orthonormal set spanning the same subspace, that is generating the same collection of linear combinations.

Gram-Schmidt 过程是一种算法,它把一组向量变换为生成同一子空间(即张成同一组线性组合)的标准正交组。

The goal of the Gram-Schmidt process is to take a linearly independent set of vectors and transform it into an orthonormal set with the same span. The first objective is to construct an orthogonal set of vectors with the same span, since from there an orthonormal set can be obtained by simply dividing each vector by its length.

Gram-Schmidt 过程的目标是取一组线性无关的向量,把它们变换为具有相同生成子空间的标准正交组。第一步是构造一组具有相同生成子空间的正交向量,因为接下来只需把每个向量除以它的长度,即得标准正交组。

算法 1:Gram-Schmidt 过程 Algorithm 1: Gram-Schmidt Process

Let $\vec{u}_{1}, \ldots, \vec{u}_{n}$ be a set of linearly independent vectors in $\mathbb{R}^{n}$.

设 $\vec{u}_{1}, \ldots, \vec{u}_{n}$ 是 $\mathbb{R}^{n}$ 中的一组线性无关向量。

I:构造一组新向量

I: Construct a new set of vectors $\vec{v}_{1}, \ldots, \vec{v}_{n}$ as follows:

I:如下构造一组新向量 $\vec{v}_{1}, \ldots, \vec{v}_{n}$:

$$\begin{array}{ll} \vec{v}_{1} & = \vec{u}_{1} \\ \vec{v}_{2} & = \vec{u}_{2} - \left( \dfrac{ \vec{u}_2 \cdot \vec{v}_1}{ \vec{v}_1^2} \right) \vec{v}_1 \\ \vec{v}_{3} & = \vec{u}_{3} - \left( \dfrac{\vec{u}_3 \cdot \vec{v}_1}{ \vec{v}_1^2} \right) \vec{v}_1 - \left( \dfrac{\vec{u}_3 \cdot \vec{v}_2}{ \vec{v}_2^2} \right) \vec{v}_2 \\ \vdots \\ \vec{v}_{n} & = \vec{u}_{n} - \left( \dfrac{\vec{u}_n \cdot \vec{v}_1}{ \vec{v}_1^2} \right) \vec{v}_1 - \left( \dfrac{\vec{u}_n \cdot \vec{v}_2}{ \vec{v}_2^2} \right) \vec{v}_2 - \cdots - \left( \dfrac{\vec{u}_n \cdot \vec{v}_{n-1}}{ \vec{v}_{n-1}^2} \right) \vec{v}_{n-1} \end{array}$$

无(公式直接给出)。

II:归一化

II: Now let $\vec{w}_{i} = \dfrac{\vec{v}_{i}}{ \vec{v}_i }$ for $i=1, \cdots ,n$.

II:现在令 $\vec{w}_{i} = \dfrac{\vec{v}_{i}}{ \vec{v}_i }$,其中 $i=1, \cdots ,n$。

Then

1. $\left\{ \vec{v}_{1}, \cdots, \vec{v}_{n} \right\}$ is an orthogonal set.

1. $\left\{ \vec{v}_{1}, \cdots, \vec{v}_{n} \right\}$ 是正交组。

2. $\left\{ \vec{w}_{1}, \cdots, \vec{w}_{n} \right\}$ is an orthonormal set.

2. $\left\{ \vec{w}_{1}, \cdots, \vec{w}_{n} \right\}$ 是标准正交组。

3. $\mathrm{span}\left\{ \vec{u}_{1}, \cdots ,\vec{u}_{n} \right\} = \mathrm{span} \left\{ \vec{v}_{1}, \cdots, \vec{v}_{n} \right\} = \mathrm{span}\left\{ \vec{w}_{1}, \cdots ,\vec{w}_{n} \right\}.$

3. $\mathrm{span}\left\{ \vec{u}_{1}, \cdots ,\vec{u}_{n} \right\} = \mathrm{span} \left\{ \vec{v}_{1}, \cdots, \vec{v}_{n} \right\} = \mathrm{span}\left\{ \vec{w}_{1}, \cdots ,\vec{w}_{n} \right\}.$

Solution

The full proof of this algorithm is beyond this material, however here is an indication of the arguments.

这个算法的完整证明超出了本材料的范围,不过下面给出论证的梗概。

To show that $\left\{ \vec{v}_{1},\cdots , \vec{v}_{n} \right\}$ is an orthogonal set, let $a_2 = \dfrac{ \vec{u}_{2} \cdot \vec{v}_{1}}{ \vec{v}_1^2} $, then:

为证明 $\left\{ \vec{v}_{1},\cdots , \vec{v}_{n} \right\}$ 是正交组,令 $a_2 = \dfrac{ \vec{u}_{2} \cdot \vec{v}_{1}}{ \vec{v}_1^2} $,则:

$$\begin{array}{ll} \vec{v}_{1} \cdot \vec{v}_{2} & = \vec{v}_{1} \cdot \left( \vec{u}_{2} - a_2 \vec{v}_{1} \right) \\ & = \vec{v}_{1} \cdot \vec{u}_{2} - a_2 (\vec{v}_{1} \cdot \vec{v}_{1}) \\ & = \vec{v}_{1} \cdot \vec{u}_{2} - \dfrac{ \vec{u}_{2} \cdot \vec{v}_{1}}{ \vec{v}_1^2} \vec{v}_{1}^2 \\ & = ( \vec{v}_{1} \cdot \vec{u}_{2} ) - ( \vec{u}_{2} \cdot \vec{v}_{1} ) = 0 \end{array}$$

无(公式直接给出)。

Now that you have shown that $\left\{ \vec{v}_{1}, \vec{v}_{2}\right\}$ is orthogonal, use the same method as above to show that $\left\{ \vec{v}_{1}, \vec{v}_{2}, \vec{v}_{3}\right\}$ is also orthogonal, and so on.

现在你已经证明 $\left\{ \vec{v}_{1}, \vec{v}_{2}\right\}$ 正交,再用同样的方法证明 $\left\{ \vec{v}_{1}, \vec{v}_{2}, \vec{v}_{3}\right\}$ 也正交,依此类推。

Then in a similar fashion you show that $\mathrm{span}\left\{ \vec{u}_{1},\cdots ,\vec{u}_{n} \right\} = \mathrm{span}\left\{ \vec{v}_{1},\cdots ,\vec{v}_{n} \right\}.$

然后以类似的方式证明 $\mathrm{span}\left\{ \vec{u}_{1},\cdots ,\vec{u}_{n} \right\} = \mathrm{span}\left\{ \vec{v}_{1},\cdots ,\vec{v}_{n} \right\}.$

Finally defining $\vec{w}_{i} = \dfrac{\vec{v}_{i}}{ \vec{v}_i }$ for $i=1, \cdots ,n$ does not affect orthogonality and yields vectors of length $1$, hence an orthonormal set. You can also observe that it does not affect the span either and the proof would be complete.

最后,对 $i=1, \cdots ,n$ 定义 $\vec{w}_{i} = \dfrac{\vec{v}_{i}}{ \vec{v}_i }$ 不影响正交性,并且得到长度为 $1$ 的向量,因而是标准正交组。你也可以看出它也不影响生成子空间,于是证明完成。

Consider the following example.

看下面的例子。

示例 9:求具有相同生成子空间的标准正交组 Find Orthonormal Set with Same Span

Consider the set of vectors $\{\vec{u}_{1}, \vec{u}_{2}\}$ given as in Example 1. That is $\vec{u}_{1}=\left[ \begin{array}{r} 1 \\ 1 \\ 0 \end{array} \right], \vec{u}_{2}=\left[ \begin{array}{r} 3 \\ 2 \\ 0 \end{array} \right] \in \mathbb{R}^{3}$.

考虑例题 1 中给出的向量组 $\{\vec{u}_{1}, \vec{u}_{2}\}$,即 $\vec{u}_{1}=\left[ \begin{array}{r} 1 \\ 1 \\ 0 \end{array} \right], \vec{u}_{2}=\left[ \begin{array}{r} 3 \\ 2 \\ 0 \end{array} \right] \in \mathbb{R}^{3}$。

Use the Gram-Schmidt algorithm to find an orthonormal set of vectors $\{\vec{w}_{1}, \vec{w}_{2}\}$ having the same span.

用 Gram-Schmidt 算法求一组具有相同生成子空间的标准正交向量组 $\{\vec{w}_{1}, \vec{w}_{2}\}$。

Solution

We already remarked that the set of vectors in $\{\vec{u}_{1}, \vec{u}_{2}\}$ is linearly independent, so we can proceed with the Gram-Schmidt algorithm:

我们已指出 $\{\vec{u}_{1}, \vec{u}_{2}\}$ 中的向量线性无关,因此可以继续执行 Gram-Schmidt 算法:

$$\begin{aligned} \vec{v}_{1} &= \vec{u}_{1} = \left[ \begin{array}{r} 1 \\ 1 \\ 0 \end{array} \right] \\ \vec{v}_{2} &= \vec{u}_{2} - \left( \dfrac{\vec{u}_2 \cdot \vec{v}_{1}}{ \vec{v}_1^2} \right) \vec{v}_{1} \\ &= \left[ \begin{array}{r} 3 \\ 2 \\ 0 \end{array} \right] - \frac{5}{2} \left[ \begin{array}{r} 1 \\ 1 \\ 0 \end{array} \right] \\ &= \left[ \begin{array}{r} \frac{1}{2} \\ - \frac{1}{2} \\ 0 \end{array} \right] \end{aligned}$$

无(公式直接给出)。

Now to normalize simply let

现在进行归一化,只需令

$$\begin{aligned} \vec{w}_{1} &= \frac{\vec{v}_{1}}{ \vec{v}_1 } = \left[ \begin{array}{r} \frac{1}{\sqrt{2}} \\ \frac{1}{\sqrt{2}} \\ 0 \end{array} \right] \\ \vec{w}_{2} &= \frac{\vec{v}_{2}}{ \vec{v}_2 } = \left[ \begin{array}{r} \frac{1}{\sqrt{2}} \\ - \frac{1}{\sqrt{2}} \\ 0 \end{array} \right] \end{aligned}$$

无(公式直接给出)。

You can verify that $\{\vec{w}_{1}, \vec{w}_{2}\}$ is an orthonormal set of vectors having the same span as $\{\vec{u}_{1}, \vec{u}_{2}\}$, namely the $XY$-plane.

你可以验证 $\{\vec{w}_{1}, \vec{w}_{2}\}$ 是与 $\{\vec{u}_{1}, \vec{u}_{2}\}$ 具有相同生成子空间(即 $XY$ 平面)的标准正交向量组。

In this example, we began with a linearly independent set and found an orthonormal set of vectors which had the same span. It turns out that if we start with a basis of a subspace and apply the Gram-Schmidt algorithm, the result will be an orthogonal basis of the same subspace. We examine this in the following example.

在本例中,我们以线性无关组出发,找到了具有相同生成子空间的标准正交向量组。事实证明,如果从某个子空间的一组基出发并应用 Gram-Schmidt 算法,结果将得到同一子空间的一组正交基。我们在下面的例子中考察这一点。

示例 10:求对应的正交基 Find a Corresponding Orthogonal Basis

Let $\vec{x}_{1}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \vec{x}_{2}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 1 \end{array}\right], \mbox{ and } \vec{x}_{3}=\left[\begin{array}{c} 1 \\ 1 \\ 0 \\ 0 \end{array}\right]$, and let $U=\mathrm{span}\{\vec{x}_{1}, \vec{x}_{2},\vec{x}_{3}\}$. Use the Gram-Schmidt Process to construct an orthogonal basis $B$ of $U$.

设 $\vec{x}_{1}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \vec{x}_{2}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 1 \end{array}\right], \mbox{ 以及 } \vec{x}_{3}=\left[\begin{array}{c} 1 \\ 1 \\ 0 \\ 0 \end{array}\right]$,并设 $U=\mathrm{span}\{\vec{x}_{1}, \vec{x}_{2},\vec{x}_{3}\}$。用 Gram-Schmidt 过程构造 $U$ 的一组正交基 $B$。

Solution

First $\vec{f}_{1}=\vec{x}_{1}$.

首先 $\vec{f}_{1}=\vec{x}_{1}$。

Next, $\vec{f}_{2}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 1 \end{array}\right] -\frac{2}{2}\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right] =\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right].$

其次,$\vec{f}_{2}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 1 \end{array}\right] -\frac{2}{2}\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right] =\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right].$

Finally, $\vec{f}_{3}=\left[\begin{array}{c} 1 \\ 1 \\ 0 \\ 0 \end{array}\right] -\frac{1}{2}\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right] -\frac{0}{1}\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right] =\left[\begin{array}{c} \frac{1}{2} \\ 1 \\ -\frac{1}{2} \\ 0 \end{array}\right].$

最后,$\vec{f}_{3}=\left[\begin{array}{c} 1 \\ 1 \\ 0 \\ 0 \end{array}\right] -\frac{1}{2}\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right] -\frac{0}{1}\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right] =\left[\begin{array}{c} \frac{1}{2} \\ 1 \\ -\frac{1}{2} \\ 0 \end{array}\right].$

Therefore, $\left\{ \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right], \left[\begin{array}{c} \frac{1}{2} \\ 1 \\ -\frac{1}{2} \\ 0 \end{array}\right] \right\}$ is an orthogonal basis of $U$. However, it is sometimes more convenient to deal with vectors having integer entries, in which case we take $B=\left\{ \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right], \left[\begin{array}{r} 1 \\ 2 \\ -1 \\ 0 \end{array}\right] \right\}.$

因此,$\left\{ \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right], \left[\begin{array}{c} \frac{1}{2} \\ 1 \\ -\frac{1}{2} \\ 0 \end{array}\right] \right\}$ 是 $U$ 的一组正交基。不过,有时处理整数分量的向量更方便,此时取 $B=\left\{ \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right], \left[\begin{array}{r} 1 \\ 2 \\ -1 \\ 0 \end{array}\right] \right\}.$

4.11 正交投影 Orthogonal Projections

An important use of the Gram-Schmidt Process is in orthogonal projections, the focus of this section.

Gram-Schmidt 过程的一个重要应用是正交投影,这正是本节的主题。

You may recall that a subspace of $\mathbb{R}^n$ is a set of vectors which contains the zero vector, and is closed under addition and scalar multiplication. Let's call such a subspace $W$. In particular, a plane in $\mathbb{R}^n$ which contains the origin, $\left(0,0, \cdots, 0 \right)$, is a subspace of $\mathbb{R}^n$.

你可能还记得,$\mathbb{R}^n$ 的子空间是包含零向量、并且对加法与数量乘法封闭的向量集合。我们称这样的子空间为 $W$。特别是,$\mathbb{R}^n$ 中包含原点 $\left(0,0, \cdots, 0 \right)$ 的平面是 $\mathbb{R}^n$ 的子空间。

Suppose a point $Y$ in $\mathbb{R}^n$ is not contained in $W$,then what point $Z$ in $W$ is closest to $Y$? Using the Gram-Schmidt Process, we can find such a point. Let $\vec{y}, \vec{z}$ represent the position vectors of the points $Y$ and $Z$ respectively, with $\vec{y}-\vec{z}$ representing the vector connecting the two points $Y$ and $Z$. It will follow that if $Z$ is the point on $W$ closest to $Y$, then $\vec{y} - \vec{z}$ will be perpendicular to $W$ (can you see why?); in other words, $\vec{y} - \vec{z}$ is orthogonal to $W$ (and to every vector contained in $W$) as in the following diagram.

设 $\mathbb{R}^n$ 中的点 $Y$ 不在 $W$ 内,那么 $W$ 中哪个点 $Z$ 离 $Y$ 最近?利用 Gram-Schmidt 过程可以找到这样的点。设 $\vec{y}, \vec{z}$ 分别是点 $Y$ 与 $Z$ 的位置向量,$\vec{y}-\vec{z}$ 是连接 $Y$ 与 $Z$ 的向量。可以推知:若 $Z$ 是 $W$ 上离 $Y$ 最近的点,则 $\vec{y} - \vec{z}$ 垂直于 $W$(你能看出为什么吗?);换言之,$\vec{y} - \vec{z}$ 与 $W$ 正交(从而与 $W$ 中的每个向量正交),如下图所示。
A plane is labeled W. Points 0 and Z line in the plane, and the vector z points from 0 to Z. Point Y is outside the plane, and vector y points from 0 to Y. A vector y minus z is drawn from the tip of y to the tip of z, and is perpendicular to the plane.
图 22(来源:LibreTexts)

The vector $\vec{z}$ is called the orthogonal projection of $\vec{y}$ on $W$. The definition is given as follows.

向量 $\vec{z}$ 称为 $\vec{y}$ 在 $W$ 上的正交投影。定义如下。

定义 8:正交投影 Orthogonal Projection

Let $W$ be a subspace of $\mathbb{R}^n$, and $Y$ be any point in $\mathbb{R}^n$. Then the orthogonal projection of $Y$ onto $W$ is given by $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right) = \left( \frac{\vec{y} \cdot \vec{w}_1}{ \vec{w}_1^2}\right) \vec{w}_1 + \left( \frac{\vec{y} \cdot \vec{w}_2}{ \vec{w}_2^2}\right) \vec{w}_2 + \cdots + \left( \frac{\vec{y} \cdot \vec{w}_m}{ \vec{w}_m^2}\right) \vec{w}_m $ where $\{\vec{w}_1, \vec{w}_2, \cdots, \vec{w}_m \}$ is any orthogonal basis of $W$.

设 $W$ 是 $\mathbb{R}^n$ 的子空间,$Y$ 是 $\mathbb{R}^n$ 中的任意一点。则 $Y$ 在 $W$ 上的正交投影为 $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right) = \left( \frac{\vec{y} \cdot \vec{w}_1}{ \vec{w}_1^2}\right) \vec{w}_1 + \left( \frac{\vec{y} \cdot \vec{w}_2}{ \vec{w}_2^2}\right) \vec{w}_2 + \cdots + \left( \frac{\vec{y} \cdot \vec{w}_m}{ \vec{w}_m^2}\right) \vec{w}_m $,其中 $\{\vec{w}_1, \vec{w}_2, \cdots, \vec{w}_m \}$ 是 $W$ 的任意一组正交基。

Therefore, in order to find the orthogonal projection, we must first find an orthogonal basis for the subspace. Note that one could use an orthonormal basis, but it is not necessary in this case since as you can see above the normalization of each vector is included in the formula for the projection.

因此,要求正交投影,必须先求出该子空间的一组正交基。注意:用标准正交基也可以,但在这种情况下并非必需,因为你在上面已经看到,每个向量的归一化已包含在投影公式当中。

Before we explore this further through an example, we show that the orthogonal projection does indeed yield a point $Z$ (the point whose position vector is the vector $\vec{z}$ above) which is the point of $W$ closest to $Y$.

在用例子进一步展开之前,我们先证明正交投影确实给出那个点 $Z$(其位置向量就是上面的向量 $\vec{z}$),即 $W$ 中离 $Y$ 最近的点。

定理 3:逼近定理 Approximation Theorem

Let $W$ be a subspace of $\mathbb{R}^n$ and $Y$ any point in $\mathbb{R}^n$. Let $Z$ be the point whose position vector is the orthogonal projection of $Y$ onto $W$.

设 $W$ 是 $\mathbb{R}^n$ 的子空间,$Y$ 是 $\mathbb{R}^n$ 中的任意一点。设 $Z$ 是位置向量为 $Y$ 在 $W$ 上的正交投影的那个点。

Then, $Z$ is the point in $W$ closest to $Y$.

则 $Z$ 是 $W$ 中离 $Y$ 最近的点。

证明

Proof. First $Z$ is certainly a point in $W$ since it is in the span of a basis of $W$.

证明:首先,$Z$ 当然是 $W$ 中的点,因为它在 $W$ 的一组基的生成子空间里。

To show that $Z$ is the point in $W$ closest to $Y$, we wish to show that $\|\vec{y}-\vec{z}_1\| > \|\vec{y}-\vec{z}\|$ for all $\vec{z}_1 \neq \vec{z} \in W$. We begin by writing $\vec{y}-\vec{z}_1 = (\vec{y} - \vec{z}) + (\vec{z} - \vec{z}_1)$. Now, the vector $\vec{y} - \vec{z}$ is orthogonal to $W$, and $\vec{z} - \vec{z}_1$ is contained in $W$. Therefore these vectors are orthogonal to each other. By the Pythagorean Theorem, we have that $\|\vec{y} - \vec{z}_1\|^2 = \|\vec{y} - \vec{z}\|^2 + \|\vec{z} -\vec{z}_1\|^2 > \|\vec{y} - \vec{z}\|^2$ This follows because $\vec{z} \neq \vec{z}_1$ so $\|\vec{z} -\vec{z}_1\|^2 > 0$.

要证明 $Z$ 是 $W$ 中离 $Y$ 最近的点,我们希望证明对所有 $\vec{z}_1 \neq \vec{z} \in W$ 都有 $\|\vec{y}-\vec{z}_1\| > \|\vec{y}-\vec{z}\|$。先把 $\vec{y}-\vec{z}_1$ 写成 $\vec{y}-\vec{z}_1 = (\vec{y} - \vec{z}) + (\vec{z} - \vec{z}_1)$。向量 $\vec{y} - \vec{z}$ 与 $W$ 正交,而 $\vec{z} - \vec{z}_1$ 在 $W$ 内,因此这两个向量彼此正交。由勾股定理,$\|\vec{y} - \vec{z}_1\|^2 = \|\vec{y} - \vec{z}\|^2 + \|\vec{z} -\vec{z}_1\|^2 > \|\vec{y} - \vec{z}\|^2$。这是因为 $\vec{z} \neq \vec{z}_1$,所以 $\|\vec{z} -\vec{z}_1\|^2 > 0$。

Hence, $\|\vec{y} - \vec{z}_1\|^2 > \|\vec{y} - \vec{z}\|^2$. Taking the square root of each side, we obtain the desired result.

从而 $\|\vec{y} - \vec{z}_1\|^2 > \|\vec{y} - \vec{z}\|^2$。两边开平方,即得所证结论。

Consider the following example.

看下面的例子。

示例 11:正交投影 Orthogonal Projection

Let $W$ be the plane through the origin given by the equation $x - 2y + z = 0$. Find the point in $W$ closest to the point $Y = (1,0,3)$.

设 $W$ 是由方程 $x - 2y + z = 0$ 给出的过原点平面。求 $W$ 中离点 $Y = (1,0,3)$ 最近的点。

Solution

We must first find an orthogonal basis for $W$. Notice that $W$ is characterized by all points $(a,b,c)$ where $c = 2b-a$. In other words, $W = \left[ \begin{array}{c} a \\ b \\ 2b - a \end{array} \right] = a \left[ \begin{array}{c} 1 \\ 0 \\ -1 \end{array} \right] + b \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right], $ a,b \in \mathbb{R}$.

必须先求出 $W$ 的一组正交基。注意 $W$ 由所有满足 $c = 2b-a$ 的点 $(a,b,c)$ 刻画。换言之,$W = \left[ \begin{array}{c} a \\ b \\ 2b - a \end{array} \right] = a \left[ \begin{array}{c} 1 \\ 0 \\ -1 \end{array} \right] + b \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right], $ a,b \in \mathbb{R}$。

We can thus write $W$ as

因此可以把 $W$ 写成

$$\begin{aligned} W &= \mbox{span} \left\{ \vec{u}_{1}, \vec{u}_{2} \right\} \\ &= \mbox{span} \left\{ \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right], \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right] \right\} \end{aligned}$$

无(公式直接给出)。

Notice that this span is a basis of $W$ as it is linearly independent. We will use the Gram-Schmidt Process to convert this to an orthogonal basis, $\{\vec{w}_{1}, \vec{w}_{2} \}$. In this case, as we remarked it is only necessary to find an orthogonal basis, and it is not required that it be orthonormal.

注意这个生成子空间由于线性无关而是 $W$ 的一组基。我们将用 Gram-Schmidt 过程把这一组变换成正交基 $\{\vec{w}_{1}, \vec{w}_{2} \}$。在这种情况下,正如我们所说,只需找到正交基即可,不要求它是标准正交基。

$$\vec{w}_{1} = \vec{u}_{1} = \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right] \\ \begin{aligned} \vec{w}_{2} &= \vec{u}_{2} - \left( \frac{ \vec{u}_{2} \cdot \vec{w}_{1}}{ \vec{w}_{1}^2} \right) \vec{w}_{1} \\ &= \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right] - \left( \frac{-2}{2}\right) \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right] \\ &= \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right] + \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right] \\ &= \left[ \begin{array}{c} 1 \\ 1 \\ 1 \end{array} \right] \end{aligned}$$

无(公式直接给出)。

Therefore an orthogonal basis of $W$ is $\left\{ \vec{w}_{1}, \vec{w}_{2} \right\} = \left\{ \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right], \left[ \begin{array}{c} 1 \\ 1 \\ 1 \end{array} \right] \right\}$.

因此 $W$ 的一组正交基是 $\left\{ \vec{w}_{1}, \vec{w}_{2} \right\} = \left\{ \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right], \left[ \begin{array}{c} 1 \\ 1 \\ 1 \end{array} \right] \right\}$。

We can now use this basis to find the orthogonal projection of the point $Y=(1,0,3)$ on the subspace $W$. We will write the position vector $\vec{y}$ of $Y$ as $\vec{y} = \left[ \begin{array}{c} 1 \\ 0 \\ 3 \end{array} \right]$. Using Definition 8, we compute the projection as follows:

现在可以用这组基求点 $Y=(1,0,3)$ 在子空间 $W$ 上的正交投影。把 $Y$ 的位置向量 $\vec{y}$ 写成 $\vec{y} = \left[ \begin{array}{c} 1 \\ 0 \\ 3 \end{array} \right]$。利用定义 8,我们如下计算投影:

$$\begin{aligned} \vec{z} &= \mathrm{proj}_{W}\left( \vec{y}\right) \\ &= \left( \frac{\vec{y} \cdot \vec{w}_1}{ \vec{w}_1^2}\right) \vec{w}_1 + \left( \frac{\vec{y} \cdot \vec{w}_2}{ \vec{w}_2^2}\right) \vec{w}_2 \\ &= \left( \frac{-2}{2} \right) \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right] + \left( \frac{4}{3} \right) \left[ \begin{array}{c} 1 \\ 1 \\ 1 \end{array} \right] \\ &= \left[ \begin{array}{c} \frac{1}{3} \\ \frac{4}{3} \\ \frac{7}{3} \end{array} \right] \end{aligned}$$

无(公式直接给出)。

Therefore the point $Z$ on $W$ closest to the point $(1,0,3)$ is $\left( \frac{1}{3}, \frac{4}{3}, \frac{7}{3} \right)$.

因此 $W$ 上离点 $(1,0,3)$ 最近的点 $Z$ 是 $\left( \frac{1}{3}, \frac{4}{3}, \frac{7}{3} \right)$。

Recall that the vector $\vec{y} - \vec{z}$ is perpendicular (orthogonal) to all the vectors contained in the plane $W$. Using a basis for $W$, we can in fact find all such vectors which are perpendicular to $W$. We call this set of vectors the orthogonal complement of $W$ and denote it $W^{\perp}$.

回忆向量 $\vec{y} - \vec{z}$ 垂直于(正交于)平面 $W$ 中所有向量。利用 $W$ 的一组基,我们其实可以找到所有与 $W$ 垂直的这类向量。我们把这样的向量集合称为 $W$ 的正交补,记作 $W^{\perp}$。

定义 9:正交补 Orthogonal Complement

Let $W$ be a subspace of $\mathbb{R}^n$. Then the orthogonal complement of $W$, written $W^{\perp}$, is the set of all vectors $\vec{x}$ such that $\vec{x} \cdot \vec{z} = 0$ for all vectors $\vec{z}$ in $W$. $W^{\perp} = \{ \vec{x} \in \mathbb{R}^n \mbox{ such that } \vec{x} \cdot \vec{z} = 0 \mbox{ for all } \vec{z} \in W \}$

设 $W$ 是 $\mathbb{R}^n$ 的子空间。则 $W$ 的正交补(记作 $W^{\perp}$)是所有满足如下条件的向量 $\vec{x}$ 的集合:对 $W$ 中所有向量 $\vec{z}$,都有 $\vec{x} \cdot \vec{z} = 0$。$W^{\perp} = \{ \vec{x} \in \mathbb{R}^n \mbox{ such that } \vec{x} \cdot \vec{z} = 0 \mbox{ for all } \vec{z} \in W \}$

The orthogonal complement is defined as the set of all vectors which are orthogonal to all vectors in the original subspace. It turns out that it is sufficient that the vectors in the orthogonal complement be orthogonal to a spanning set of the original space.

正交补定义为所有与原子空间中每个向量都正交的向量的集合。事实证明,只要正交补中的向量与原来空间的一组生成向量正交即可。

命题 3:与生成组正交 Orthogonal to Spanning Set

Let $W$ be a subspace of $\mathbb{R}^n$ such that $W = \mathrm{span} \left\{ \vec{w}_{1}, \vec{w}_{2}, \cdots, \vec{w}_{m} \right\}$. Then $W^{\perp}$ is the set of all vectors which are orthogonal to each $\vec{w}_{i}$ in the spanning set.

设 $W$ 是 $\mathbb{R}^n$ 的子空间,且 $W = \mathrm{span} \left\{ \vec{w}_{1}, \vec{w}_{2}, \cdots, \vec{w}_{m} \right\}$。则 $W^{\perp}$ 是所有与生成组中每个 $\vec{w}_{i}$ 都正交的向量的集合。

The following proposition demonstrates that the orthogonal complement of a subspace is itself a subspace.

下面的命题说明,子空间的正交补本身也是子空间。

命题 4:正交补 The Orthogonal Complement

Let $W$ be a subspace of $\mathbb{R}^n$. Then the orthogonal complement $W^{\perp}$ is also a subspace of $\mathbb{R}^n$.

设 $W$ 是 $\mathbb{R}^n$ 的子空间。则正交补 $W^{\perp}$ 也是 $\mathbb{R}^n$ 的子空间。

Consider the following proposition.

考察下面这则命题。

命题 5:$\mathbb{R}^n$ 的正交补 Orthogonal Complement of $\mathbb{R}^n$

The complement of $\mathbb{R}^n$ is the set containing the zero vector: $(\mathbb{R}^n)^{\perp} = \left\{ \vec{0} \right\}$. Similarly, $\left\{ \vec{0} \right\}^{\perp} = (\mathbb{R}^n)$.

$\mathbb{R}^n$ 的补集是只含零向量的集合:$(\mathbb{R}^n)^{\perp} = \left\{ \vec{0} \right\}$。类似地,$\left\{ \vec{0} \right\}^{\perp} = \mathbb{R}^n$。

证明

Proof. Here, $\vec{0}$ is the zero vector of $\mathbb{R}^n$. Since $\vec{x}\cdot\vec{0}=0$ for all $\vec{x}\in\mathbb{R}^n$, $\mathbb{R}^n\subseteq \{\vec{0}\}^{\perp}$. Since $\{\vec{0}\}^{\perp}\subseteq\mathbb{R}^n$, the equality follows, i.e., $\{\vec{0}\}^{\perp}=\mathbb{R}^n$.

证明:这里 $\vec{0}$ 是 $\mathbb{R}^n$ 的零向量。由于对一切 $\vec{x}\in\mathbb{R}^n$ 有 $\vec{x}\cdot\vec{0}=0$,故 $\mathbb{R}^n\subseteq \{\vec{0}\}^{\perp}$。又因为 $\{\vec{0}\}^{\perp}\subseteq\mathbb{R}^n$,即得等式,也就是 $\{\vec{0}\}^{\perp}=\mathbb{R}^n$。

Again, since $\vec{x}\cdot\vec{0}=0$ for all $\vec{x}\in\mathbb{R}^n$, $\vec{0}\in (\mathbb{R}^n)^{\perp}$, so $\{\vec{0}\}\subseteq(\mathbb{R}^n)^{\perp}$. Suppose $\vec{x}\in\mathbb{R}^n$, $\vec{x}\neq\vec{0}$. Since $\vec{x}\cdot\vec{x}=\|\vec{x}\|^2$ and $\vec{x}\neq\vec{0}$, $\vec{x}\cdot\vec{x}\neq 0$, so $\vec{x}\not\in(\mathbb{R}^n)^{\perp}$. Therefore $(\mathbb{R}^n)^{\perp}\subseteq \{\vec{0}\}$, and thus $(\mathbb{R}^n)^{\perp}=\{\vec{0}\}$.

同样地,由于对一切 $\vec{x}\in\mathbb{R}^n$ 有 $\vec{x}\cdot\vec{0}=0$,故 $\vec{0}\in (\mathbb{R}^n)^{\perp}$,从而 $\{\vec{0}\}\subseteq(\mathbb{R}^n)^{\perp}$。设 $\vec{x}\in\mathbb{R}^n$,$\vec{x}\neq\vec{0}$。因为 $\vec{x}\cdot\vec{x}=\|\vec{x}\|^2$ 且 $\vec{x}\neq\vec{0}$,所以 $\vec{x}\cdot\vec{x}\neq 0$,因此 $\vec{x}\not\in(\mathbb{R}^n)^{\perp}$。故 $(\mathbb{R}^n)^{\perp}\subseteq \{\vec{0}\}$,从而 $(\mathbb{R}^n)^{\perp}=\{\vec{0}\}$。

In the next example, we will look at how to find $W^{\perp}$.

在下面的例子中,我们看如何求 $W^{\perp}$。

示例 12:正交补 Orthogonal Complement

Let $W$ be the plane through the origin given by the equation $x - 2y + z = 0$. Find a basis for the orthogonal complement of $W$.

设 $W$ 是由方程 $x - 2y + z = 0$ 给出的过原点平面。求 $W$ 的正交补的一组基。

Solution

From Example 11 we know that we can write $W$ as $W = \mbox{span} \left\{ \vec{u}_{1}, \vec{u}_{2} \right\} = \mbox{span} \left\{ \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right], \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right] \right\}$.

由示例 11 可知,可以把 $W$ 写成 $W = \mbox{span} \left\{ \vec{u}_{1}, \vec{u}_{2} \right\} = \mbox{span} \left\{ \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right], \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right] \right\}$。

In order to find $W^{\perp}$, we need to find all $\vec{x}$ which are orthogonal to every vector in this span.

为求 $W^{\perp}$,需要找到所有与该生成子空间中每个向量都正交的 $\vec{x}$。

Let $\vec{x} = \left[ \begin{array}{c} x_1 \\ x_2 \\ x_3 \end{array} \right]$. In order to satisfy $\vec{x} \cdot \vec{u}_{1} = 0$, the following equation must hold. $x_1 - x_3 = 0$

设 $\vec{x} = \left[ \begin{array}{c} x_1 \\ x_2 \\ x_3 \end{array} \right]$。要满足 $\vec{x} \cdot \vec{u}_{1} = 0$,下面这个方程必须成立:$x_1 - x_3 = 0$

In order to satisfy $\vec{x} \cdot \vec{u}_{2} = 0$, the following equation must hold. $x_2 + 2x_3 = 0$

要满足 $\vec{x} \cdot \vec{u}_{2} = 0$,下面这个方程必须成立:$x_2 + 2x_3 = 0$

Both of these equations must be satisfied, so we have the following system of equations.

这两个方程都必须满足,因此有下面这个方程组。

$$\begin{array}{c} x_1 - x_3 = 0 \\ x_2 + 2x_3 = 0 \end{array}$$

无(公式直接给出)。

To solve, set up the augmented matrix.

求解时,先写出增广矩阵。

$$\left[ \begin{array}{rrr|r} 1 & 0 & -1 & 0 \\ 0 & 1 & 2 & 0 \end{array} \right]$$

无(公式直接给出)。

Using Gaussian Elimination, we find that $W^{\perp} = \mbox{span} \left\{ \left[ \begin{array}{r} 1 \\ -2 \\ 1 \end{array} \right] \right\}$, and hence $\left\{ \left[ \begin{array}{r} 1 \\ -2 \\ 1 \end{array} \right] \right\}$ is a basis for $W^{\perp}$.

用高斯消元法求得 $W^{\perp} = \mbox{span} \left\{ \left[ \begin{array}{r} 1 \\ -2 \\ 1 \end{array} \right] \right\}$,从而 $\left\{ \left[ \begin{array}{r} 1 \\ -2 \\ 1 \end{array} \right] \right\}$ 是 $W^{\perp}$ 的一组基。

The following results summarize the important properties of the orthogonal projection.

下面的结论总结了正交投影的重要性质。

定理 4:正交投影 Orthogonal Projection

Let $W$ be a subspace of $\mathbb{R}^n$, $Y$ be any point in $\mathbb{R}^n$, and let $Z$ be the point in $W$ closest to $Y$. Then,

设 $W$ 是 $\mathbb{R}^n$ 的子空间,$Y$ 是 $\mathbb{R}^n$ 中的任意一点,$Z$ 是 $W$ 中离 $Y$ 最近的点。则:

1. The position vector $\vec{z}$ of the point $Z$ is given by $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right)$

1. 点 $Z$ 的位置向量 $\vec{z}$ 由 $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right)$ 给出

2. $\vec{z} \in W$ and $\vec{y} - \vec{z} \in W^{\perp}$

2. $\vec{z} \in W$ 且 $\vec{y} - \vec{z} \in W^{\perp}$

3. $\| Y - Z \| < \| Y - Z_1 \|$ for all $Z_1 \neq Z \in W$

3. 对所有 $Z_1 \neq Z \in W$,有 $\| Y - Z \| < \| Y - Z_1 \|$

Consider the following example of this concept.

考虑这一概念下面的例子。

示例 13:求离已知向量最近的向量 Find a Vector Closest to a Given Vector

Let $\vec{x}_{1}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \vec{x}_{2}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 1 \end{array}\right], \vec{x}_{3}=\left[\begin{array}{c} 1 \\ 1 \\ 0 \\ 0 \end{array}\right], \mbox{ and } \vec{v}=\left[\begin{array}{c} 4 \\ 3 \\ -2 \\ 5 \end{array}\right]$. We want to find the vector in $W =\mathrm{span}\{\vec{x}_{1}, \vec{x}_{2},\vec{x}_{3}\}$ closest to $\vec{y}$.

设 $\vec{x}_{1}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \vec{x}_{2}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 1 \end{array}\right], \vec{x}_{3}=\left[\begin{array}{c} 1 \\ 1 \\ 0 \\ 0 \end{array}\right], \mbox{ 以及 } \vec{v}=\left[\begin{array}{c} 4 \\ 3 \\ -2 \\ 5 \end{array}\right]$。我们想求 $W =\mathrm{span}\{\vec{x}_{1}, \vec{x}_{2},\vec{x}_{3}\}$ 中离 $\vec{y}$ 最近的向量。

Solution

We will first use the Gram-Schmidt Process to construct the orthogonal basis, $B$, of $W$: $B=\left\{ \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right], \left[\begin{array}{r} 1 \\ 2 \\ -1 \\ 0 \end{array}\right] \right\}.$

首先用 Gram-Schmidt 过程构造 $W$ 的正交基 $B$:$B=\left\{ \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right], \left[\begin{array}{r} 1 \\ 2 \\ -1 \\ 0 \end{array}\right] \right\}.$

By Theorem 4, $\mathrm{proj}_{U}(\vec{v}) = \frac{2}{2} \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right] + \frac{5}{1}\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right] + \frac{12}{6}\left[\begin{array}{r} 1 \\ 2 \\ -1 \\ 0 \end{array}\right] = \left[\begin{array}{r} 3 \\ 4 \\ -1 \\ 5 \end{array}\right]$ is the vector in $U$ closest to $\vec{y}$.

由定理 4,$\mathrm{proj}_{U}(\vec{v}) = \frac{2}{2} \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right] + \frac{5}{1}\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right] + \frac{12}{6}\left[\begin{array}{r} 1 \\ 2 \\ -1 \\ 0 \end{array}\right] = \left[\begin{array}{r} 3 \\ 4 \\ -1 \\ 5 \end{array}\right]$ 是 $U$ 中离 $\vec{y}$ 最近的向量。

Consider the next example.

看下一个例子。

示例 14:把向量写成两个向量之和 Vector Written as a Sum of Two Vectors

Let $W$ be a subspace given by $W = \mbox{span} \left\{ \left[ \begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array} \right], \left[ \begin{array}{c} 0 \\ 1 \\ 0 \\ 2 \end{array} \right] \right\}$, and $Y = (1,2,3,4)$. Find the point $Z$ in $W$ closest to $Y$, and moreover write $\vec{y}$ as the sum of a vector in $W$ and a vector in $W^{\perp}$.

设 $W$ 是由 $W = \mbox{span} \left\{ \left[ \begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array} \right], \left[ \begin{array}{c} 0 \\ 1 \\ 0 \\ 2 \end{array} \right] \right\}$ 给出的子空间,且 $Y = (1,2,3,4)$。求 $W$ 中离 $Y$ 最近的点 $Z$,并把 $\vec{y}$ 写成 $W$ 中一个向量与 $W^{\perp}$ 中一个向量之和。

Solution

From Theorem 3 the point $Z$ in $W$ closest to $Y$ is given by $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right)$.

由定理 3,$W$ 中离 $Y$ 最近的点 $Z$ 由 $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right)$ 给出。

Notice that since the above vectors already give an orthogonal basis for $W$, we have:

注意由于上述向量已经给出 $W$ 的一组正交基,我们有:

$$\begin{aligned} \vec{z} &= \mathrm{proj}_{W}\left( \vec{y}\right) \\ &= \left( \frac{\vec{y} \cdot \vec{w}_{1}}{ \vec{w}_{1}^2}\right) \vec{w}_{1} + \left( \frac{\vec{y} \cdot \vec{w}_{2}}{ \vec{w}_{2}^2}\right) \vec{w}_{2} \\ &= \left( \frac{4}{2} \right) \left[ \begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array} \right] + \left( \frac{10}{5} \right) \left[ \begin{array}{c} 0 \\ 1 \\ 0 \\ 2 \end{array} \right] \\ &= \left[ \begin{array}{c} 2 \\ 2 \\ 2 \\ 4 \end{array} \right] \end{aligned}$$

无(公式直接给出)。

Therefore the point in $W$ closest to $Y$ is $Z = (2,2,2,4)$. Now, we need to write $\vec{y}$ as the sum of a vector in $W$ and a vector in $W^{\perp}$. This can easily be done as follows: $\vec{y} = \vec{z} + (\vec{y} - \vec{z})$ since $\vec{z}$ is in $W$ and as we have seen $\vec{y} - \vec{z}$ is in $W^{\perp}$. The vector $\vec{y} - \vec{z}$ is given by $\vec{y} - \vec{z} = \left[ \begin{array}{c} 1 \\ 2 \\ 3 \\ 4 \end{array} \right] - \left[ \begin{array}{c} 2 \\ 2 \\ 2 \\ 4 \end{array} \right] = \left[ \begin{array}{r} -1 \\ 0 \\ 1 \\ 0 \end{array} \right]$ Therefore, we can write $\vec{y}$ as $\left[ \begin{array}{c} 1 \\ 2 \\ 3 \\ 4 \end{array} \right] = \left[ \begin{array}{c} 2 \\ 2 \\ 2 \\ 4 \end{array} \right] + \left[ \begin{array}{r} -1 \\ 0 \\ 1 \\ 0 \end{array} \right]$

因此 $W$ 中离 $Y$ 最近的点是 $Z = (2,2,2,4)$。现在,需要把 $\vec{y}$ 写成 $W$ 中一个向量与 $W^{\perp}$ 中一个向量之和。这很容易做到:$\vec{y} = \vec{z} + (\vec{y} - \vec{z})$,因为 $\vec{z}$ 在 $W$ 中,且如我们所知 $\vec{y} - \vec{z}$ 在 $W^{\perp}$ 中。向量 $\vec{y} - \vec{z}$ 为 $\vec{y} - \vec{z} = \left[ \begin{array}{c} 1 \\ 2 \\ 3 \\ 4 \end{array} \right] - \left[ \begin{array}{c} 2 \\ 2 \\ 2 \\ 4 \end{array} \right] = \left[ \begin{array}{r} -1 \\ 0 \\ 1 \\ 0 \end{array} \right]$。因此可以把 $\vec{y}$ 写成 $\left[ \begin{array}{c} 1 \\ 2 \\ 3 \\ 4 \end{array} \right] = \left[ \begin{array}{c} 2 \\ 2 \\ 2 \\ 4 \end{array} \right] + \left[ \begin{array}{r} -1 \\ 0 \\ 1 \\ 0 \end{array} \right]$

Example 15: Point in a Plane Closest to a Given Point

示例 15:平面上离已知点最近的点

Find the point $Z$ in the plane $3x+y-2z=0$ that is closest to the point $Y=(1,1,1)$.

求平面 $3x+y-2z=0$ 中离点 $Y=(1,1,1)$ 最近的点 $Z$。

Solution

The solution will proceed as follows.

求解过程如下。

1. Find a basis $X$ of the subspace $W$ of $\mathbb{R}^3$ defined by the equation $3x+y-2z=0$. 2. Orthogonalize the basis $X$ to get an orthogonal basis $B$ of $W$. 3. Find the projection on $W$ of the position vector of the point $Y$.

1. 求出由方程 $3x+y-2z=0$ 定义的 $\mathbb{R}^3$ 的子空间 $W$ 的一组基 $X$。2. 把基 $X$ 正交化,得到 $W$ 的一组正交基 $B$。3. 求点 $Y$ 的位置向量在 $W$ 上的投影。

We now begin the solution.

现在开始求解。

1. $3x+y-2z=0$ is a system of one equation in three variables. Putting the augmented matrix in reduced row-echelon form: $\left[\begin{array}{rrr|r} 3 & 1 & -2 & 0 \end{array}\right] \rightarrow \left[\begin{array}{rrr|r} 1 & \frac{1}{3} & -\frac{2}{3} & 0 \end{array}\right]$ gives general solution $x=\frac{1}{3}s+\frac{2}{3}t$, $y=s$, $z=t$ for any $s,t\in\mathbb{R}$. Then $W=\mathrm{span} \left\{ \left[\begin{array}{r} -\frac{1}{3} \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{r} \frac{2}{3} \\ 0 \\ 1 \end{array}\right]\right\}$. Let $X=\left\{ \left[\begin{array}{r} -1 \\ 3 \\ 0 \end{array}\right], \left[\begin{array}{r} 2 \\ 0 \\ 3 \end{array}\right]\right\}$. Then $X$ is linearly independent and $\mathrm{span}(X)=W$, so $X$ is a basis of $W$.

1. $3x+y-2z=0$ 是三个变量组成的一个方程。把增广矩阵化为简化行梯形:$\left[\begin{array}{rrr|r} 3 & 1 & -2 & 0 \end{array}\right] \rightarrow \left[\begin{array}{rrr|r} 1 & \frac{1}{3} & -\frac{2}{3} & 0 \end{array}\right]$ 得通解 $x=\frac{1}{3}s+\frac{2}{3}t$,$y=s$,$z=t$,其中 $s,t\in\mathbb{R}$ 任意。则 $W=\mathrm{span} \left\{ \left[\begin{array}{r} -\frac{1}{3} \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{r} \frac{2}{3} \\ 0 \\ 1 \end{array}\right]\right\}$。取 $X=\left\{ \left[\begin{array}{r} -1 \\ 3 \\ 0 \end{array}\right], \left[\begin{array}{r} 2 \\ 0 \\ 3 \end{array}\right]\right\}$。则 $X$ 线性无关且 $\mathrm{span}(X)=W$,所以 $X$ 是 $W$ 的一组基。

2. Use the Gram-Schmidt Process to get an orthogonal basis of $W$: $\vec{f}_{1}=\left[\begin{array}{r} -1 \\ 3 \\ 0\end{array}\right]\mbox{ and }\vec{f}_{2}=\left[\begin{array}{r}2 \\ 0 \\ 3\end{array}\right]-\frac{-2}{10}\left[\begin{array}{r} -1 \\ 3 \\ 0\end{array}\right]=\frac{1}{5}\left[\begin{array}{r}9 \\ 3 \\ 15\end{array}\right].$ Therefore $B=\left\{\left[\begin{array}{r}-1\\3\\0\end{array}\right], \left[\begin{array}{r}3\\1\\5 \end{array}\right]\right\}$ is an orthogonal basis of $W$.

2. 用 Gram-Schmidt 过程求 $W$ 的一组正交基:$\vec{f}_{1}=\left[\begin{array}{r} -1 \\ 3 \\ 0\end{array}\right]\mbox{ 且 }\vec{f}_{2}=\left[\begin{array}{r}2 \\ 0 \\ 3\end{array}\right]-\frac{-2}{10}\left[\begin{array}{r} -1 \\ 3 \\ 0\end{array}\right]=\frac{1}{5}\left[\begin{array}{r}9 \\ 3 \\ 15\end{array}\right].$ 因此 $B=\left\{\left[\begin{array}{r}-1\\3\\0\end{array}\right], \left[\begin{array}{r}3\\1\\5 \end{array}\right]\right\}$ 是 $W$ 的一组正交基。

3. To find the point $Z$ on $W$ closest to $Y=(1,1,1)$, compute $\begin{aligned} \mathrm{proj}_{W}\left[\begin{array}{r} 1 \\ 1 \\ 1 \end{array}\right] & = \frac{2}{10} \left[\begin{array}{r} -1 \\ 3 \\ 0 \end{array}\right] + \frac{9}{35}\left[\begin{array}{r} 3 \\ 1 \\ 5 \end{array}\right] \\ & = \frac{1}{7}\left[\begin{array}{r} 4 \\ 6 \\ 9 \end{array}\right].\end{aligned}$ Therefore, $Z=\left( \frac{4}{7}, \frac{6}{7}, \frac{9}{7}\right)$.

3. 为求 $W$ 上离 $Y=(1,1,1)$ 最近的点 $Z$,计算 $\begin{aligned} \mathrm{proj}_{W}\left[\begin{array}{r} 1 \\ 1 \\ 1 \end{array}\right] & = \frac{2}{10} \left[\begin{array}{r} -1 \\ 3 \\ 0 \end{array}\right] + \frac{9}{35}\left[\begin{array}{r} 3 \\ 1 \\ 5 \end{array}\right] \\ & = \frac{1}{7}\left[\begin{array}{r} 4 \\ 6 \\ 9 \end{array}\right].\end{aligned}$ 因此,$Z=\left( \frac{4}{7}, \frac{6}{7}, \frac{9}{7}\right)$。

4.12 最小二乘逼近 Least Squares Approximation

It should not be surprising to hear that many problems do not have a perfect solution, and in these cases the objective is always to try to do the best possible. For example what does one do if there are no solutions to a system of linear equations $A\vec{x}=\vec{b}$? It turns out that what we do is find $\vec{x}$ such that $A\vec{x}$ is as close to $\vec{b}$ as possible. A very important technique that follows from orthogonal projections is that of the least square approximation, and allows us to do exactly that.

很多问题没有完美的解,这不应当令人惊讶;在这些情况下,目标总是尽量做到最好。例如,若线性方程组 $A\vec{x}=\vec{b}$ 无解,该怎么办?事实证明,我们做的是去找 $\vec{x}$,使得 $A\vec{x}$ 尽可能接近 $\vec{b}$。由正交投影导出一个非常重要的技巧,即最小二乘逼近,它能让我们恰好做到这一点。

We begin with a lemma.

我们从一个引理开始。

Recall that we can form the image of an $m \times n$ matrix $A$ by $\mathrm{im}\left( A\right) = \left\{ A\vec{x} : \vec{x} \in \mathbb{R}^n \right\}$. Rephrasing Theorem 4 using the subspace $W=\mathrm{im}\left( A\right)$ gives the equivalence of an orthogonality condition with a minimization condition. The following picture illustrates this orthogonality condition and geometric meaning of this theorem.

回忆我们可以用 $A$ 的像 $\mathrm{im}\left( A\right) = \left\{ A\vec{x} : \vec{x} \in \mathbb{R}^n \right\}$ 表示 $m \times n$ 矩阵 $A$。用子空间 $W=\mathrm{im}\left( A\right)$ 重新表述定理 4,就得到正交条件与极小化条件的等价性。下图说明了这一定理的正交条件与几何意义。
A plane is labeled A of R n. From 0, vectors u and z equals A x are drawn in the plane. Vector y points outside the plane. A vector from the tip of y to the tip of u is shown. A vector y minus z from the tip of y to the tip of z is shown and is perpendicular to the plane.
图 23(来源:LibreTexts)

定理 5:极小元的存在性 Existence of Minimizers

Let $\vec{y}\in \mathbb{R}^{m}$ and let $A$ be an $m\times n$ matrix.

设 $\vec{y}\in \mathbb{R}^{m}$,$A$ 是 $m\times n$ 矩阵。

Choose $\vec{z}\in W= \mathrm{im}\left( A\right)$ given by $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right)$, and let $\vec{x} \in \mathbb{R}^{n}$ such that $\vec{z}=A\vec{x}$.

取 $W= \mathrm{im}\left( A\right)$ 中由 $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right)$ 给出的 $\vec{z}$,并取 $\vec{x} \in \mathbb{R}^{n}$ 使得 $\vec{z}=A\vec{x}$。

Then

1. $\vec{y} - A\vec{x} \in W^{\perp}$

1. $\vec{y} - A\vec{x} \in W^{\perp}$

2. $\|\vec{y} - A\vec{x}\| < \|\vec{y} - \vec{u}\|$ for all $\vec{u} \neq \vec{z} \in W$

2. 对所有 $\vec{u} \neq \vec{z} \in W$,有 $\|\vec{y} - A\vec{x}\| < \|\vec{y} - \vec{u}\|$

We note a simple but useful observation.

我们注意一个简单而有用的观察。

引理 1:转置与点积 Transpose and Dot Product

Let $A$ be an $m\times n$ matrix. Then $A\vec{x} \cdot \vec{y} = \vec{x}\cdot A^T\vec{y}$

设 $A$ 是 $m\times n$ 矩阵。则 $A\vec{x} \cdot \vec{y} = \vec{x}\cdot A^T\vec{y}$

证明

Proof. This follows from the definitions: $A\vec{x} \cdot \vec{y}=\sum_{i,j}a_{ij}x_{j} y_{i} =\sum_{i,j}x_{j} a_{ji} y_{i}= \vec{x} \cdot A^T\vec{y}$

证明:这由定义直接得出:$A\vec{x} \cdot \vec{y}=\sum_{i,j}a_{ij}x_{j} y_{i} =\sum_{i,j}x_{j} a_{ji} y_{i}= \vec{x} \cdot A^T\vec{y}$

The next corollary gives the technique of least squares.

下面的推论给出最小二乘的技巧。

推论 1:最小二乘与正规方程 Least Squares and Normal Equation

A specific value of $\vec{x}$ which solves the problem of Theorem 5 is obtained by solving the equation $A^TA\vec{x}=A^T\vec{y}$ Furthermore, there always exists a solution to this system of equations.

解定理 5 的问题所需的具体 $\vec{x}$ 可通过解方程 $A^TA\vec{x}=A^T\vec{y}$ 得到。此外,该方程组总有解。

证明

Proof. For $\vec{x}$ the minimizer of Theorem 5, $\left( \vec{y}-A\vec{x}\right) \cdot A \vec{u} =0$ for all $\vec{u} \in \mathbb{R}^{n}$ and from Lemma 1, this is the same as saying $A^T\left( \vec{y}-A\vec{x}\right) \cdot \vec{u}=0$ for all $u \in \mathbb{R}^{n}.$ This implies $A^T\vec{y}-A^TA\vec{x}=\vec{0}.$ Therefore, there is a solution to the equation of this corollary, and it solves the minimization problem of Theorem 5.

证明:对定理 5 的极小元 $\vec{x}$,对所有 $\vec{u} \in \mathbb{R}^{n}$ 都有 $\left( \vec{y}-A\vec{x}\right) \cdot A \vec{u} =0$。由引理 1,这等价于对所有 $u \in \mathbb{R}^{n}$ 有 $A^T\left( \vec{y}-A\vec{x}\right) \cdot \vec{u}=0$,从而 $A^T\vec{y}-A^TA\vec{x}=\vec{0}.$ 因此,本推论的方程有解,且它解出了定理 5 的极小化问题。

Note that $\vec{x}$ might not be unique but $A\vec{x}$, the closest point of $A\left(\mathbb{R}^{n}\right)$ to $\vec{y}$, is unique as was shown in the above argument.

注意 $\vec{x}$ 可能不唯一,但 $A\left(\mathbb{R}^{n}\right)$ 中离 $\vec{y}$ 最近的点 $A\vec{x}$ 是唯一的,如上面的论证所示。

Consider the following example.

看下面的例子。

示例 16:方程组的最小二乘解 Least Squares Solution to a System

Find a least squares solution to the system $\left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 2 \\ 1 \\ 1 \end{array} \right]$

求方程组 $\left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 2 \\ 1 \\ 1 \end{array} \right]$ 的最小二乘解。

Solution

First, consider whether there exists a real solution. To do so, set up the augmented matrix given by $\left[ \begin{array}{rr|r} 2 & 1 & 2 \\ -1 & 3 & 1 \\ 4 & 5 & 1 \end{array} \right]$ The reduced row-echelon form of this augmented matrix is $\left[ \begin{array}{rr|r} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right]$

首先,考虑是否存在实数解。为此,写出增广矩阵 $\left[ \begin{array}{rr|r} 2 & 1 & 2 \\ -1 & 3 & 1 \\ 4 & 5 & 1 \end{array} \right]$。该增广矩阵的简化行梯形是 $\left[ \begin{array}{rr|r} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right]$。

It follows that there is no real solution to this system. Therefore we wish to find the least squares solution. The normal equations are $\begin{aligned} A^T A \vec{x} &= A^T \vec{y} \\ \left[ \begin{array}{rrr} 2 & -1 & 4 \\ 1 & 3 & 5 \end{array} \right] \left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] &=\left[ \begin{array}{rrr} 2 & -1 & 4 \\ 1 & 3 & 5 \end{array} \right] \left[ \begin{array}{c} 2 \\ 1 \\ 1 \end{array} \right]\end{aligned}$ and so we need to solve the system $\left[ \begin{array}{rr} 21 & 19 \\ 19 & 35 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{r} 7 \\ 10 \end{array} \right]$ This is a familiar exercise and the solution is $\left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} \frac{5}{34} \\ \frac{7}{34} \end{array} \right]$

由此可知该方程组没有实数解,因此我们希望求最小二乘解。正规方程为 $\begin{aligned} A^T A \vec{x} &= A^T \vec{y} \\ \left[ \begin{array}{rrr} 2 & -1 & 4 \\ 1 & 3 & 5 \end{array} \right] \left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] &=\left[ \begin{array}{rrr} 2 & -1 & 4 \\ 1 & 3 & 5 \end{array} \right] \left[ \begin{array}{c} 2 \\ 1 \\ 1 \end{array} \right]\end{aligned}$。因此需要解方程组 $\left[ \begin{array}{rr} 21 & 19 \\ 19 & 35 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{r} 7 \\ 10 \end{array} \right]$。这是一个熟悉的练习,解为 $\left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} \frac{5}{34} \\ \frac{7}{34} \end{array} \right]$。

Consider another example.

再看另一个例子。

示例 17:方程组的最小二乘解 Least Squares Solution to a System

Find a least squares solution to the system $\left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 3 \\ 2 \\ 9 \end{array} \right]$

求方程组 $\left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 3 \\ 2 \\ 9 \end{array} \right]$ 的最小二乘解。

Solution

First, consider whether there exists a real solution. To do so, set up the augmented matrix given by $\left[ \begin{array}{rr|r} 2 & 1 & 3 \\ -1 & 3 & 2 \\ 4 & 5 & 9 \end{array} \right]$ The reduced row-echelon form of this augmented matrix is $\left[ \begin{array}{rr|r} 1 & 0 & 1 \\ 0 & 1 & 1 \\ 0 & 0 & 0 \end{array} \right]$

首先,考虑是否存在实数解。为此,写出增广矩阵 $\left[ \begin{array}{rr|r} 2 & 1 & 3 \\ -1 & 3 & 2 \\ 4 & 5 & 9 \end{array} \right]$。该增广矩阵的简化行梯形是 $\left[ \begin{array}{rr|r} 1 & 0 & 1 \\ 0 & 1 & 1 \\ 0 & 0 & 0 \end{array} \right]$。

It follows that the system has a solution given by $x=y=1$. However we can also use the normal equations and find the least squares solution. $\left[ \begin{array}{rrr} 2 & -1 & 4 \\ 1 & 3 & 5 \end{array} \right] \left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{rrr} 2 & -1 & 4 \\ 1 & 3 & 5 \end{array} \right] \left[ \begin{array}{r} 3 \\ 2 \\ 9 \end{array} \right]$ Then $\left[ \begin{array}{rr} 21 & 19 \\ 19 & 35 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 40 \\ 54 \end{array} \right]$

由此可知方程组有解 $x=y=1$。不过我们也可以用正规方程求最小二乘解。$\left[ \begin{array}{rrr} 2 & -1 & 4 \\ 1 & 3 & 5 \end{array} \right] \left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{rrr} 2 & -1 & 4 \\ 1 & 3 & 5 \end{array} \right] \left[ \begin{array}{r} 3 \\ 2 \\ 9 \end{array} \right]$,于是 $\left[ \begin{array}{rr} 21 & 19 \\ 19 & 35 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 40 \\ 54 \end{array} \right]$。

The least squares solution is $\left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 1 \\ 1 \end{array} \right]$ which is the same as the solution found above.

最小二乘解为 $\left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 1 \\ 1 \end{array} \right]$,与上面求得的解相同。

An important application of Corollary 1 is the problem of finding the least squares regression line in statistics. Suppose you are given points in the $xy$ plane $\left\{ \left( x_{1},y_{1}\right), \left( x_{2},y_{2}\right), \cdots, \left( x_{n},y_{n}\right) \right\}$ and you would like to find constants $m$ and $b$ such that the line $\vec{y}=m\vec{x}+b$ goes through all these points. Of course this will be impossible in general. Therefore, we try to find $m,b$ such that the line will be as close as possible. The desired system is

推论 1 的一个重要应用是统计学中求最小二乘回归直线的问题。设在 $xy$ 平面中给定一些点 $\left\{ \left( x_{1},y_{1}\right), \left( x_{2},y_{2}\right), \cdots, \left( x_{n},y_{n}\right) \right\}$,你想求常数 $m$ 与 $b$,使得直线 $\vec{y}=m\vec{x}+b$ 经过所有这些点。当然一般而言这不可能。因此我们尝试找 $m,b$,使直线尽可能贴近这些点。所需方程组为

$$\left[ \begin{array}{c} y_{1} \\ \vdots \\ y_{n} \end{array} \right] =\left[ \begin{array}{cc} x_{1} & 1 \\ \vdots & \vdots \\ x_{n} & 1 \end{array} \right] \left[ \begin{array}{c} m \\ b \end{array} \right]$$

无(公式直接给出)。

which is of the form $\vec{y}=A\vec{x}$. It is desired to choose $m$ and $b$ to make

这就是 $\vec{y}=A\vec{x}$ 的形式。我们希望选取 $m$ 与 $b$,使

$$\left\| A\left[ \begin{array}{c} m \\ b \end{array} \right] -\left[ \begin{array}{c} y_{1} \\ \vdots \\ y_{n} \end{array} \right] \right\|^{2}$$

无(公式直接给出)。

as small as possible. According to Theorem 5 and Corollary 1, the best values for $m$ and $b$ occur as the solution to

尽可能小。根据定理 5 与推论 1,$m$ 与 $b$ 的最佳值是下面方程的解。

$$A^{T}A\left[ \begin{array}{c} m \\ b \end{array} \right] =A^{T}\left[ \begin{array}{c} y_{1} \\ \vdots \\ y_{n} \end{array} \right] ,\\ \mbox{where}\\ A=\left[ \begin{array}{cc} x_{1} & 1 \\ \vdots & \vdots \\ x_{n} & 1 \end{array} \right]$$

无(公式直接给出)。

Thus, computing $A^{T}A,$

于是计算 $A^{T}A,$

$$\left[ \begin{array}{cc} \sum_{i=1}^{n}x_{i}^{2} & \sum_{i=1}^{n}x_{i} \\ \sum_{i=1}^{n}x_{i} & n \end{array} \right] \left[ \begin{array}{c} m \\ b \end{array} \right] =\left[ \begin{array}{c} \sum_{i=1}^{n}x_{i}y_{i} \\ \sum_{i=1}^{n}y_{i} \end{array} \right]$$

无(公式直接给出)。

Solving this system of equations for $m$ and $b$ (using Cramer's rule for example) yields:

解这个含未知量 $m$ 与 $b$ 的方程组(例如用克拉默法则)得:

$$m= \frac{-\left( \sum_{i=1}^{n}x_{i}\right) \left( \sum_{i=1}^{n}y_{i}\right) +\left( \sum_{i=1}^{n}x_{i}y_{i}\right) n}{\left( \sum_{i=1}^{n}x_{i}^{2}\right) n-\left( \sum_{i=1}^{n}x_{i}\right)^{2}} ,\quad b=\frac{-\left( \sum_{i=1}^{n}x_{i}\right) \sum_{i=1}^{n}x_{i}y_{i}+\left( \sum_{i=1}^{n}y_{i}\right) \sum_{i=1}^{n}x_{i}^{2}}{\left( \sum_{i=1}^{n}x_{i}^{2}\right) n-\left( \sum_{i=1}^{n}x_{i}\right)^{2}}$$

无(公式直接给出)。

Consider the following example.

看下面的例子。

示例 18:最小二乘回归 Least Squares Regression

Find the least squares regression line $\vec{y}=m\vec{x}+b$ for the following set of data points: $\left\{ (0,1), (1,2), (2,2), (3,4), (4,5) \right\}$

对下面这组数据点求最小二乘回归直线 $\vec{y}=m\vec{x}+b$:$\left\{ (0,1), (1,2), (2,2), (3,4), (4,5) \right\}$

Solution

In this case we have $n=5$ data points and we obtain: $\begin{array}{ll} \sum_{i=1}^{5}x_{i} = 10 & \sum_{i=1}^{5}y_{i} = 14 \\ \sum_{i=1}^{5}x_{i}y_{i} = 38 & \sum_{i=1}^{5}x_{i}^{2} = 30 \end{array}$ and hence $\begin{aligned} m &= \frac{- 10 \cdot 14 + 5 \cdot 38}{5\cdot 30-10^2} = 1.00 \\ b &= \frac{- 10 \cdot 38 + 14\cdot 30}{5\cdot 30-10^2} = 0.80 \end{aligned}$

在这种情况下有 $n=5$ 个数据点,得到:$\begin{array}{ll} \sum_{i=1}^{5}x_{i} = 10 & \sum_{i=1}^{5}y_{i} = 14 \\ \sum_{i=1}^{5}x_{i}y_{i} = 38 & \sum_{i=1}^{5}x_{i}^{2} = 30 \end{array}$,从而 $\begin{aligned} m &= \frac{- 10 \cdot 14 + 5 \cdot 38}{5\cdot 30-10^2} = 1.00 \\ b &= \frac{- 10 \cdot 38 + 14\cdot 30}{5\cdot 30-10^2} = 0.80 \end{aligned}$。

The least squares regression line for the set of data points is: $\vec{y} = \vec{x}+.8$

这组数据点的最小二乘回归直线为:$\vec{y} = \vec{x}+.8$

One could use this line to approximate other values for the data. For example for $x=6$ one could use $y(6)=6+.8=6.8$ as an approximate value for the data.

可以用这条直线近似数据的其他取值。例如当 $x=6$ 时,可用 $y(6)=6+.8=6.8$ 作为数据的近似值。

The following diagram shows the data points and the corresponding regression line.

下图显示数据点及相应的回归直线。
2D graph showing 5 data points that are not perfectly in a line, and a straight regression line that is close to all the data points.
图 24(来源:LibreTexts)

One could clearly do a least squares fit for curves of the form $y=ax^{2}+bx+c$ in the same way. In this case you want to solve as well as possible for $a,b,$ and $c$ the system $\left[ \begin{array}{ccc} x_{1}^{2} & x_{1} & 1 \\ \vdots & \vdots & \vdots \\ x_{n}^{2} & x_{n} & 1 \end{array} \right] \left[ \begin{array}{c} a \\ b \\ c \end{array} \right] =\left[ \begin{array}{c} y_{1} \\ \vdots \\ y_{n} \end{array} \right]$ and one would use the same technique as above. Many other similar problems are important, including many in higher dimensions and they are all solved the same way.

显然也可以用同样的方式对形如 $y=ax^{2}+bx+c$ 的曲线做最小二乘拟合。这种情况下,你要尽量解出关于 $a,b,c$ 的方程组 $\left[ \begin{array}{ccc} x_{1}^{2} & x_{1} & 1 \\ \vdots & \vdots & \vdots \\ x_{n}^{2} & x_{n} & 1 \end{array} \right] \left[ \begin{array}{c} a \\ b \\ c \end{array} \right] =\left[ \begin{array}{c} y_{1} \\ \vdots \\ y_{n} \end{array} \right]$,并且用与上面相同的技巧。许多其他类似的问题(包括大量高维问题)都很重要,它们都以同样的方式求解。

4.12 应用 Applications

学习目标 Outcomes

1. Apply the concepts of vectors in $\mathbb{R}^n$ to the applications of physics and work.

1. 将 $\mathbb{R}^n$ 中向量的概念应用于物理与功的应用。

向量与物理 Vectors and Physics

Suppose you push on something. Then, your push is made up of two components, how hard you push and the direction you push. This illustrates the concept of force.

设想你去推某个东西。那么,你的推力由两个成分构成:推动的力度和推动的方向。这体现了力的概念。

定义 1:力 Force

Force is a vector. The magnitude of this vector is a measure of how hard it is pushing. It is measured in units such as Newtons or pounds or tons. The direction of this vector is the direction in which the push is taking place.

是一个向量。这个向量的大小是推动强劲程度的度量。它用诸如牛顿、磅或吨等单位来度量。这个向量的方向就是推力作用的方向。

Vectors are used to model force and other physical vectors like velocity. As with all vectors, a vector modeling force has two essential ingredients, its magnitude and its direction.

向量被用来刻画力以及诸如速度之类的其他物理向量。与所有向量一样,刻画力的向量有两个基本要素:大小与方向。

Recall the special vectors which point along the coordinate axes. These are given by $\vec{e}_{i} = \left[ 0 \cdots 0 \ 1 \ 0 \cdots 0 \right]^T$ where the $1$ is in the $i^{th}$ slot and there are zeros in all the other spaces. The direction of $\vec{e}_{i}$ is referred to as the $i^{th}$ direction.

回忆那些沿坐标轴方向的特殊向量。它们由 $\vec{e}_{i} = \left[ 0 \cdots 0 \ 1 \ 0 \cdots 0 \right]^T$ 给出,其中 $1$ 位于第 $i$ 个位置,其余位置全为 $0$。$\vec{e}_{i}$ 的方向称为第 $i$ 个方向。

Consider the following picture which illustrates the case of $\mathbb{R}^{3}.$ Recall that in $\mathbb{R}^3$, we may refer to these vectors as $\vec{i}, \vec{j},$ and $\vec{k}$.

考虑下面说明 $\mathbb{R}^{3}$ 情形的图。回忆在 $\mathbb{R}^3$ 中,我们常把这些向量记为 $\vec{i}, \vec{j},$ 与 $\vec{k}$。
3D graph with 3 vectors of the same length. the vector labeled e1 pointing in the x direction, e2 pointing in the y direction, and e3 pointing in the z direction
图 25(来源:LibreTexts)

Given a vector $\vec{u}=\left[ u_{1} \cdots u_{n}\right]^T ,$ it follows that $\vec{u}=u_{1}\vec{e}_{1}+\cdots +u_{n}\vec{e}_{n}= \sum_{k=1}^{n}u_{i}\vec{e}_{i}$

给定向量 $\vec{u}=\left[ u_{1} \cdots u_{n}\right]^T ,$,则有 $\vec{u}=u_{1}\vec{e}_{1}+\cdots +u_{n}\vec{e}_{n}= \sum_{k=1}^{n}u_{i}\vec{e}_{i}$。

What does addition of vectors mean physically? Suppose two forces are applied to some object. Each of these would be represented by a force vector and the two forces acting together would yield an overall force acting on the object which would also be a force vector known as the resultant. Suppose the two vectors are $\vec{u}=\sum_{k=1}^{n}u_{i}\vec{e}_{i}$ and $\vec{v}=\sum_{k=1}^{n}v_{i}\vec{e}_{i}$. Then the vector $\vec{u}$ involves a component in the $i^{th}$ direction given by $u_{i}\vec{e}_{i}$, while the component in the $i^{th}$ direction of $\vec{v}$ is $v_{i}\vec{e}_{i}.$ Then the vector $\vec{u} + \vec{v}$ should have a component in the $i^{th}$ direction equal to $\left( u_{i}+v_{i}\right) \vec{e}_{i}.$ This is exactly what is obtained when the vectors, $\vec{u}$ and $\vec{v}$ are added. $\begin{aligned} \vec{u}+\vec{v}& =\left[ u_{1}+v_{1} \cdots u_{n}+v_{n}\right]^T \\ & =\sum_{i=1}^{n}\left( u_{i}+v_{i}\right) \vec{e}_{i}\end{aligned}$

向量加法的物理意义是什么?设对某个物体施加两个力。其中每个力都由一个力向量表示,两个力共同作用产生一个作用在该物体上的合力,这也是一个力向量,称为合力。设两个向量是 $\vec{u}=\sum_{k=1}^{n}u_{i}\vec{e}_{i}$ 与 $\vec{v}=\sum_{k=1}^{n}v_{i}\vec{e}_{i}$。则 $\vec{u}$ 在第 $i$ 个方向上有分量 $u_{i}\vec{e}_{i}$,而 $\vec{v}$ 在第 $i$ 个方向上的分量为 $v_{i}\vec{e}_{i}.$ 那么向量 $\vec{u} + \vec{v}$ 在第 $i$ 个方向上的分量应等于 $\left( u_{i}+v_{i}\right) \vec{e}_{i}.$ 这正是把 $\vec{u}$ 与 $\vec{v}$ 相加时得到的结果:$\begin{aligned} \vec{u}+\vec{v}& =\left[ u_{1}+v_{1} \cdots u_{n}+v_{n}\right]^T \\ & =\sum_{i=1}^{n}\left( u_{i}+v_{i}\right) \vec{e}_{i}\end{aligned}$。

Thus the addition of vectors according to the rules of addition in $\mathbb{R}^{n}$ which were presented earlier, yields the appropriate vector which duplicates the cumulative effect of all the vectors in the sum.

因此,按前面给出的 $\mathbb{R}^{n}$ 中向量加法规则进行加法,得到恰当的向量,它复现了求和项中所有向量的累积效果。

Consider now some examples of vector addition.

现在考虑向量加法的几个例子。

示例 1:三个力的合力 The Resultant of Three Forces

There are three ropes attached to a car and three people pull on these ropes. The first exerts a force of $\vec{F}_{1} = 2\vec{i} + 3\vec{j} -2 \vec{k}$ Newtons, the second exerts a force of $\vec{F}_{2} = 3\vec{i}+5\vec{j}+\vec{k}$ Newtons and the third exerts a force of $5\vec{i}-\vec{j}+2\vec{k}$ Newtons. Find the total force in the direction of $\vec{i}$.

有三根绳子系在一辆汽车上,三人在拉这些绳子。第一个人施加的力为 $\vec{F}_{1} = 2\vec{i} + 3\vec{j} -2 \vec{k}$ 牛顿,第二个人施加的力为 $\vec{F}_{2} = 3\vec{i}+5\vec{j}+\vec{k}$ 牛顿,第三个人施加的力为 $5\vec{i}-\vec{j}+2\vec{k}$ 牛顿。求 $\vec{i}$ 方向上的总力。

Solution

To find the total force, we add the vectors as described above. This is given by $\begin{aligned} &(2\vec{i}+3\vec{j}-2\vec{k}) + (3\vec{i}+5\vec{j}+\vec{k}) + (5\vec{i}-\vec{j}+2\vec{k}) \\ &= (2 + 3 + 5) \vec{i} + (3 + 5 - 1) \vec{j} + (-2+1+2) \vec{k} \\ &= 10 \vec{i} + 7 \vec{j} + \vec{k}\end{aligned}$ Hence, the total force is $10\vec{i}+7\vec{j}+\vec{k}$ Newtons. Therefore, the force in the $\vec{i}$ direction is $10$ Newtons.

为求总力,按上面所述把各向量相加。即 $\begin{aligned} &(2\vec{i}+3\vec{j}-2\vec{k}) + (3\vec{i}+5\vec{j}+\vec{k}) + (5\vec{i}-\vec{j}+2\vec{k}) \\ &= (2 + 3 + 5) \vec{i} + (3 + 5 - 1) \vec{j} + (-2+1+2) \vec{k} \\ &= 10 \vec{i} + 7 \vec{j} + \vec{k}\end{aligned}$。因此,总力为 $10\vec{i}+7\vec{j}+\vec{k}$ 牛顿。所以 $\vec{i}$ 方向上的力是 $10$ 牛顿。

Consider another example.

再看另一个例子。

示例 2:由几何描述求向量 Finding a Vector from Geometric Description

An airplane flies North East at $100$ miles per hour. Write this as a vector.

一架飞机以每小时 $100$ 英里的速度向东北方向飞行。把它写成一个向量。

Solution

A picture of this situation follows.

这种情况的示意图如下。
a vector pointing to the upper right
图 26(来源:LibreTexts)

Therefore, we need to find the vector $\vec{u}$ which has length $100$ and direction as shown in this diagram. We can consider the vector $\vec{u}$ as the hypotenuse of a right triangle having equal sides, since the direction of $\vec{u}$ corresponds with the $45 ^{\circ}$ line. The sides, corresponding to the $\vec{i}$ and $\vec{j}$ directions, should be each of length $100/\sqrt{2}.$ Therefore, the vector is given by $\vec{u} = \frac{100}{\sqrt{2}} \vec{i}+ \frac{100}{\sqrt{2 }}\vec{j} = \left[ \begin{array}{rr} \frac{100}{\sqrt{2}} & \frac{100}{\sqrt{2}} \end{array} \right]^T$

因此,我们需要找长度 $100$、方向如该图所示的向量 $\vec{u}$。因为 $\vec{u}$ 的方向对应 $45 ^{\circ}$ 斜线,可以把 $\vec{u}$ 看作两条直角边相等的直角三角形之斜边。对应 $\vec{i}$ 与 $\vec{j}$ 方向的两条边都应各长 $100/\sqrt{2}.$ 因此该向量为 $\vec{u} = \frac{100}{\sqrt{2}} \vec{i}+ \frac{100}{\sqrt{2 }}\vec{j} = \left[ \begin{array}{rr} \frac{100}{\sqrt{2}} & \frac{100}{\sqrt{2}} \end{array} \right]^T$。

This example also motivates the concept of velocity, defined below.

这个例子也引出了下面定义的速度概念。

定义 2:速率与速度 Speed and Velocity

The speed of an object is a measure of how fast it is going. It is measured in units of length per unit time. For example, miles per hour, kilometers per minute, feet per second. The velocity is a vector having the speed as the magnitude but also specifying the direction.

物体的速率是它运动快慢的度量,用每单位时间所经长度的单位度量,例如英里每小时、千米每分钟、英尺每秒。速度是以速率为大小、同时指明方向的向量。

Thus the velocity vector in the above example is $\frac{100}{\sqrt{2}}\vec{i}+ \frac{100}{\sqrt{2}}\vec{j}$, while the speed is $100$ miles per hour.

因此上面例子中的速度向量是 $\frac{100}{\sqrt{2}}\vec{i}+ \frac{100}{\sqrt{2}}\vec{j}$,而速率是 $100$ 英里每小时。

Consider the following example.

考虑下面的例子。

The velocity of an airplane is $100\vec{i}+\vec{j}+\vec{k}$ measured in kilometers per hour and at a certain instant of time its position is $\left( 1,2,1\right) .$ Find the position of this airplane one minute later.

一架飞机的速度是 $100\vec{i}+\vec{j}+\vec{k}$(以千米每小时为单位),在某一时刻它的位置是 $\left( 1,2,1\right) .$ 求一分钟后这架飞机的位置。

Solution

Here imagine a Cartesian coordinate system in which the third component is altitude and the first and second components are measured on a line from West to East and a line from South to North.

这里设想一个笛卡尔坐标系,其中第三个分量是高度,第一、二个分量分别沿自西向东与自南向北的直线度量。

Consider the vector $\left[ \begin{array}{rrr} 1 & 2 & 1 \end{array} \right]^T ,$ which is the initial position vector of the airplane. As the plane moves, the position vector changes according to the velocity vector. After one minute (considered as $\frac{1}{60}$ of an hour) the airplane has moved in the $\vec{i}$ direction a distance of $100\times \frac{1}{60}= \frac{5}{3}$ kilometer. In the $\vec{j}$ direction it has moved $\frac{1}{60}$ kilometer during this same time, while it moves $\frac{1}{60}$ kilometer in the $\vec{k}$ direction. Therefore, the new displacement vector for the airplane is $\left[ \begin{array}{rrr} 1 & 2 & 1 \end{array} \right]^T + \left[ \begin{array}{rrr} \frac{5}{3} & \frac{1}{60} & \frac{1}{60} \end{array} \right]^T =\left[ \begin{array}{rrr} \frac{8}{3} & \frac{121}{60} & \frac{121}{60} \end{array} \right]^T$

考虑向量 $\left[ \begin{array}{rrr} 1 & 2 & 1 \end{array} \right]^T ,$,它是飞机的初始位置向量。飞机运动时,位置向量按速度向量变化。一分钟后(看作 $\frac{1}{60}$ 小时),飞机在 $\vec{i}$ 方向移动了 $100\times \frac{1}{60}= \frac{5}{3}$ 千米。在同一段时间内它在 $\vec{j}$ 方向移动了 $\frac{1}{60}$ 千米,在 $\vec{k}$ 方向移动了 $\frac{1}{60}$ 千米。因此飞机新的位移向量为 $\left[ \begin{array}{rrr} 1 & 2 & 1 \end{array} \right]^T + \left[ \begin{array}{rrr} \frac{5}{3} & \frac{1}{60} & \frac{1}{60} \end{array} \right]^T =\left[ \begin{array}{rrr} \frac{8}{3} & \frac{121}{60} & \frac{121}{60} \end{array} \right]^T$。

Now consider an example which involves combining two velocities.

现在考虑一个涉及合成两个速度的例子。

示例 4:两个速度之和 Sum of Two Velocities

A certain river is one half kilometer wide with a current flowing at $4$ kilometers per hour from East to West. A man swims directly toward the opposite shore from the South bank of the river at a speed of $3$ kilometers per hour. How far down the river does he find himself when he has swam across? How far does he end up swimming?

某条河宽半千米,水流以每小时 $4$ 千米的速度自东向西流动。一个人从河南岸以每小时 $3$ 千米的速度径直游向对岸。他游到对岸时发现自己被冲到下游多远?他总共游了多远?

Solution

Consider the following picture which demonstrates the above scenario.

考虑下面演示上述情境的图。
an illustration with a vector labeled 4 pointing down the middle of a river, and a vector labeled 3 pointing perpendicular to it
图 27(来源:LibreTexts)

First we want to know the total time of the swim across the river. The velocity in the direction across the river is $3$ kilometers per hour, and the river is $\frac{1}{2}$ kilometer wide. It follows the trip takes $1/6$ hour or $10$ minutes.

首先想知道横渡河流所花的总时间。横渡方向上的速度是 $3$ 千米每小时,河宽 $\frac{1}{2}$ 千米。由此可知渡河需 $1/6$ 小时,即 $10$ 分钟。

Now, we can compute how far downstream he will end up. Since the river runs at a rate of $4$ kilometers per hour, and the trip takes $1/6$ hour, the distance traveled downstream is given by $4 \left(\frac{1}{6}\right) = \frac{2}{3}$ kilometers.

现在可以计算他最终被冲到下游多远。由于河流以 $4$ 千米每小时的速度流动,而渡河耗时 $1/6$ 小时,所以向下游漂移的距离为 $4 \left(\frac{1}{6}\right) = \frac{2}{3}$ 千米。

The distance traveled by the swimmer is given by the hypotenuse of a right triangle. The two arms of the triangle are given by the distance across the river, $\frac{1}{2}$ km, and the distance traveled downstream, $\frac{2}{3}$ km. Then, using the Pythagorean Theorem, we can calculate the total distance $d$ traveled. $d = \sqrt{ \left(\frac{2}{3} \right)^2 + \left( \frac{1}{2} \right) ^2 } = \frac{5}{6} \mbox{ km}$

游泳者游过的距离由直角三角形的斜边给出。三角形两条直角边分别是横渡距离 $\frac{1}{2}$ 千米与下游漂移距离 $\frac{2}{3}$ 千米。再用勾股定理即可算出总路程 $d$:$d = \sqrt{ \left(\frac{2}{3} \right)^2 + \left( \frac{1}{2} \right) ^2 } = \frac{5}{6} \mbox{ km}$。

Therefore, the swimmer travels a total distance of $\frac{5}{6}$ kilometers.

因此,游泳者总共游了 $\frac{5}{6}$ 千米。

Work

The mathematical concept of work is an application of vectors in $\mathbb{R}^n$. The physical concept of work differs from the notion of work employed in ordinary conversation. For example, suppose you were to slide a $150$ pound weight off a table which is three feet high and shuffle along the floor for $50$ yards, keeping the height always three feet and then deposit this weight on another three foot high table. The physical concept of work would indicate that the force exerted by your arms did no work during this project. The reason for this definition is that even though your arms exerted considerable force on the weight, the direction of motion was at right angles to the force they exerted. The only part of a force which does work in the sense of physics is the component of the force in the direction of motion.

功的数学概念是 $\mathbb{R}^n$ 中向量的一个应用。功的物理概念与日常谈话中"工作"的含义不同。例如,设想你把一块 $150$ 磅的重物从三英尺高的桌上滑下,在地板上拖着走了 $50$ 码,始终把高度保持在三英尺,然后把这重物放到另一张三英尺高的桌上。从功的物理概念看,你的手臂施加的力在这整个过程中没有做功。作此定义的理由是:尽管手臂对重物施加了相当大的力,但运动方向与手臂施加的力方向成直角。就物理意义而言,一个力中只有沿运动方向的分量才做功。

Work is defined to be the magnitude of the component of this force times the distance over which it acts, when the component of force points in the direction of motion. In the case where the force points in exactly the opposite direction of motion work is given by $\left( -1\right)$ times the magnitude of this component times the distance. Thus the work done by a force on an object as the object moves from one point to another is a measure of the extent to which the force contributes to the motion. This is illustrated in the following picture in the case where the given force contributes to the motion.

功定义为:当力的分量指向运动方向时,这个力分量的大小乘以它作用的距离。在力的方向恰好与运动方向相反的情形下,功为该分量的大小取负后乘以距离。因此,物体从一点移动到另一点时力对物体所做的功,是度量这个力对运动贡献程度的一个量。下图在给定力促进运动的情形下说明这一点。
Illustration with a vector from point P to Q and a vector F at an angle of theta from PQ. A vector F-perpendicular is drawn perpendicular to PQ, and a vector F-parallel is drawn parallel to PQ, where F is the same of F-perpendicular and F-parallel.
图 28(来源:LibreTexts)

Recall that for any vector $\vec{u}$ in $\mathbb{R}^n$, we can write $\vec{u}$ as a sum of two vectors, as in $\vec{u} = \vec{u}_{\|} + \vec{u}_{\perp}$ For any force $\vec{F}$, we can write this force as the sum of a vector in the direction of the motion and a vector perpendicular to the motion. In other words, $\vec{F} = \vec{F}_{\|} + \vec{F}_{\bot}$

回忆对 $\mathbb{R}^n$ 中任何向量 $\vec{u}$,都可以写成两个向量之和,如 $\vec{u} = \vec{u}_{\|} + \vec{u}_{\perp}$。对任何力 $\vec{F}$,都可以把这个力写成沿运动方向的一个向量与垂直于运动方向的一个向量之和。换言之,$\vec{F} = \vec{F}_{\|} + \vec{F}_{\bot}$。

In the above picture the force, $\vec{F}$ is applied to an object which moves on the straight line from $P$ to $Q.$ There are two vectors shown, $\vec{F}_{\|}$ and $\vec{F}_{\bot }$ and the picture is intended to indicate that when you add these two vectors you get $\vec{F}$. In other words, $\vec{F} = \vec{F}_{\|} + \vec{F}_{\bot}$. Notice that $\vec{F}_{\|}$ acts in the direction of motion and $\vec{F}_{\bot }$ acts perpendicular to the direction of motion. Only $\vec{F}_{\|}$ contributes to the work done by $\vec{F}$ on the object as it moves from $P$ to $Q$. $\vec{F}_{\|}$ is called the component of the force in the direction of motion. From trigonometry, you see the magnitude of $\vec{F}_{\|}$ should equal $\|\vec{F}\| \left\| \cos \theta \right\| .$ Thus, since $\vec{F}_{\|}$ points in the direction of the vector from $P$ to $Q,$ the total work done should equal $\|\vec{F}\| \|\vec{PQ}\| \cos \theta = \|\vec{F}\| \|\vec{q}-\vec{p}\| \cos \theta$

在上图中,力 $\vec{F}$ 施加在一个从 $P$ 沿直线移动到 $Q$ 的物体上。图中显示两个向量 $\vec{F}_{\|}$ 与 $\vec{F}_{\bot }$,此图意在表明把这两个向量相加即得 $\vec{F}$。换言之,$\vec{F} = \vec{F}_{\|} + \vec{F}_{\bot}$。注意 $\vec{F}_{\|}$ 沿运动方向作用,而 $\vec{F}_{\bot }$ 垂直于运动方向作用。在物体从 $P$ 移动到 $Q$ 的过程中,只有 $\vec{F}_{\|}$ 对 $\vec{F}$ 所做的功有贡献。$\vec{F}_{\|}$ 称为力在运动方向上的分量。由三角学可知,$\vec{F}_{\|}$ 的大小应等于 $\|\vec{F}\| \left\| \cos \theta \right\| .$ 由于 $\vec{F}_{\|}$ 指向 $P$ 到 $Q$ 的向量方向,因此所做的总功应等于 $\|\vec{F}\| \|\vec{PQ}\| \cos \theta = \|\vec{F}\| \|\vec{q}-\vec{p}\| \cos \theta$。

Now, suppose the included angle had been obtuse. Then the work done by the force $\vec{F}$ on the object would have been negative because $\vec{F}_{\|}$ would point in $-1$ times the direction of the motion. In this case, $\cos \theta$ would also be negative and so it is still the case that the work done would be given by the above formula. Thus from the geometric description of the dot product given above, the work equals $\|\vec{F}\| \|\vec{q}-\vec{p}\| \cos \theta =\vec{F}\bullet \left( \vec{q}-\vec{p}\right)$ This explains the following definition.

现在,设想夹角为钝角。那么力 $\vec{F}$ 对物体所做的功将为负,因为 $\vec{F}_{\|}$ 指向 $-1$ 倍的运动方向。此时 $\cos \theta$ 也为负,所以所做的功仍由上面的公式给出。于是由前面给出的点积几何描述,功等于 $\|\vec{F}\| \|\vec{q}-\vec{p}\| \cos \theta =\vec{F}\bullet \left( \vec{q}-\vec{p}\right)$。这就解释了下面的定义。

定义 3:力对物体所做的功 Work Done on an Object by a Force

Let $\vec{F}$ be a force acting on an object which moves from the point $P$ to the point $Q$, which have position vectors given by $\vec{p}$ and $\vec{q}$ respectively. Then the work done on the object by the given force equals $\vec{F}\bullet \left( \vec{q}-\vec{p}\right) .$

设 $\vec{F}$ 是作用在一个物体上的力,该物体从点 $P$ 移动到点 $Q$,其位置向量分别为 $\vec{p}$ 与 $\vec{q}$。则给定力对该物体所做的等于 $\vec{F}\bullet \left( \vec{q}-\vec{p}\right) .$

Consider the following example.

看下面的例子。

示例 4:求功 Finding Work

Let $\vec{F}= \left[ \begin{array}{rrr} 2 & 7 & -3 \end{array} \right]^T$ Newtons. Find the work done by this force in moving from the point $\left( 1,2,3\right)$ to the point $\left( -9,-3,4\right)$ along the straight line segment joining these points where distances are measured in meters.

设 $\vec{F}= \left[ \begin{array}{rrr} 2 & 7 & -3 \end{array} \right]^T$ 牛顿。求这个力沿连接这两点的直线段把物体从点 $\left( 1,2,3\right)$ 移动到点 $\left( -9,-3,4\right)$ 所做的功,其中距离以米为单位度量。

Solution

First, compute the vector $\vec{q} - \vec{p}$, given by $\left[ \begin{array}{rrr} -9 & -3 & 4 \end{array} \right]^T - \left[ \begin{array}{rrr} 1 & 2 & 3 \end{array} \right]^T = \left[ \begin{array}{rrr} -10 & -5 & 1 \end{array} \right]^T$

首先计算向量 $\vec{q} - \vec{p}$:$\left[ \begin{array}{rrr} -9 & -3 & 4 \end{array} \right]^T - \left[ \begin{array}{rrr} 1 & 2 & 3 \end{array} \right]^T = \left[ \begin{array}{rrr} -10 & -5 & 1 \end{array} \right]^T$。

According to Definition 3 the work done is $\begin{aligned} \left[ \begin{array}{rrr} 2 & 7 & 3 \end{array} \right]^T \bullet \left[ \begin{array}{rrr} -10 & -5 & 1 \end{array} \right]^T & =-20+\left( -35\right) +\left( -3\right) \\ & =-58 \text{ Newton meters}\end{aligned}$

根据定义 3,所做的功为 $\begin{aligned} \left[ \begin{array}{rrr} 2 & 7 & 3 \end{array} \right]^T \bullet \left[ \begin{array}{rrr} -10 & -5 & 1 \end{array} \right]^T & =-20+\left( -35\right) +\left( -3\right) \\ & =-58 \text{ 牛顿·米}\end{aligned}$。

Note that if the force had been given in pounds and the distance had been given in feet, the units on the work would have been foot pounds. In general, work has units equal to units of a force times units of a length. Recall that $1$ Newton meter is equal to $1$ Joule. Also notice that the work done by the force can be negative as in the above example.

注意,如果力以磅为单位、距离以英尺为单位,则功的单位就是磅·英尺。一般地,功的单位等于力的单位乘以长度的单位。回忆 $1$ 牛顿·米等于 $1$ 焦耳。还要注意,如上面例子所示,力所做的功可以为负。

习题 Exercises

习题 1 Exercise 1

Find $-3\left[\begin{array}{c}5\\-1\\2\\-3\end{array}\right]+5\left[\begin{array}{c}-8\\2\\-3\\6\end{array}\right]$.

求 $-3\left[\begin{array}{c}5\\-1\\2\\-3\end{array}\right]+5\left[\begin{array}{c}-8\\2\\-3\\6\end{array}\right]$。

答案 Answer:$\left[\begin{array}{c}-55\\13\\-21\\39\end{array}\right]$

答案:$\left[\begin{array}{c}-55\\13\\-21\\39\end{array}\right]$

习题 2 Exercise 2

Find $-7\left[\begin{array}{c}6\\0\\4\\-1\end{array}\right]+6\left[\begin{array}{c}-13\\-1\\1\\6\end{array}\right]$.

求 $-7\left[\begin{array}{c}6\\0\\4\\-1\end{array}\right]+6\left[\begin{array}{c}-13\\-1\\1\\6\end{array}\right]$。

习题 3 Exercise 3

Decide whether $\vec{v}=\left[\begin{array}{c}4\\4\\-3\end{array}\right]$ is a linear combination of the vectors $\vec{u}_{1}=\left[\begin{array}{c}3\\1\\-1\end{array}\right]\quad\text{and}\quad\vec{u}_{2}=\left[\begin{array}{c}2\\-2\\1\end{array}\right].$

判断 $\vec{v}=\left[\begin{array}{c}4\\4\\-3\end{array}\right]$ 是否向量 $\vec{u}_{1}=\left[\begin{array}{c}3\\1\\-1\end{array}\right]$ 与 $\vec{u}_{2}=\left[\begin{array}{c}2\\-2\\1\end{array}\right]$ 的线性组合。

答案 Answer:$\left[\begin{array}{c}4\\4\\-3\end{array}\right]=2\left[\begin{array}{c}3\\1\\-1\end{array}\right]-\left[\begin{array}{c}2\\-2\\1\end{array}\right]$

答案:$\left[\begin{array}{c}4\\4\\-3\end{array}\right]=2\left[\begin{array}{c}3\\1\\-1\end{array}\right]-\left[\begin{array}{c}2\\-2\\1\end{array}\right]$

习题 4 Exercise 4

Decide whether $\vec{v}=\left[\begin{array}{c}4\\4\\4\end{array}\right]$ is a linear combination of the vectors $\vec{u}_{1}=\left[\begin{array}{c}3\\1\\-1\end{array}\right]\quad\text{and}\quad\vec{u}_{2}=\left[\begin{array}{c}2\\-2\\1\end{array}\right].$

判断 $\vec{v}=\left[\begin{array}{c}4\\4\\4\end{array}\right]$ 是否向量 $\vec{u}_{1}=\left[\begin{array}{c}3\\1\\-1\end{array}\right]$ 与 $\vec{u}_{2}=\left[\begin{array}{c}2\\-2\\1\end{array}\right]$ 的线性组合。

答案 Answer:The system $\left[\begin{array}{c}4\\4\\4\end{array}\right]=a_1\left[\begin{array}{c}3\\1\\-1\end{array}\right]+a_2\left[\begin{array}{c}2\\-2\\1\end{array}\right]$ has no solution.

答案:方程组 $\left[\begin{array}{c}4\\4\\4\end{array}\right]=a_1\left[\begin{array}{c}3\\1\\-1\end{array}\right]+a_2\left[\begin{array}{c}2\\-2\\1\end{array}\right]$ 无解。

习题 5 Exercise 5

Find the vector equation for the line through $(-7, 6, 0)$ and $(-1, 1, 4)$. Then, find the parametric equations for this line.

求经过 $(-7, 6, 0)$ 与 $(-1, 1, 4)$ 两点的直线的向量方程,再求这条直线的参数方程。

习题 6 Exercise 6

Find parametric equations for the line through the point $(7, 7, 1)$ with a direction vector $\vec{d}=\left[\begin{array}{c}1\\6\\2\end{array}\right]$.

求经过点 $(7, 7, 1)$、方向向量为 $\vec{d}=\left[\begin{array}{c}1\\6\\2\end{array}\right]$ 的直线的参数方程。

习题 7 Exercise 7

Parametric equations of the line are $\begin{aligned}x&=t+2 \\ y&=6-3t \\ x&=-t=6\end{aligned}$ Find a direction vector for the line and a point on the line.

直线的参数方程为 $\begin{aligned}x&=t+2 \\ y&=6-3t \\ x&=-t=6\end{aligned}$。求这直线的一个方向向量和直线上一点。

习题 8 Exercise 8

Find the vector equation for the line through the two points $(-5, 5, 1),$ $(2, 2, 4)$. Then, find the parametric equations.

求经过 $(-5, 5, 1),$ $(2, 2, 4)$ 两点的直线的向量方程,再求参数方程。

习题 9 Exercise 9

The equation of a line in two dimensions is written as $y = x-5$. Find parametric equations for this line.

二维中一条直线的方程写作 $y = x-5$。求这条直线的参数方程。

习题 10 Exercise 10

Find parametric equations for the line through $(6, 5,-2)$ and $(5, 1, 2)$.

求经过 $(6, 5,-2)$ 与 $(5, 1, 2)$ 两点的直线的参数方程。

习题 11 Exercise 11

Find the vector equation and parametric equations for the line through the point $(-7, 10,-6)$ with a direction vector $\vec{d}=\left[\begin{array}{c}1\\1\\3\end{array}\right]$.

求经过点 $(-7, 10,-6)$、方向向量为 $\vec{d}=\left[\begin{array}{c}1\\1\\3\end{array}\right]$ 的直线的向量方程与参数方程。

习题 12 Exercise 12

Parametric equations of the line are $\begin{aligned}x&=2t+2 \\ y&=5-4t \\ z&=-t-3\end{aligned}$ Find a direction vector for the line and a point on the line, and write the vector equation of the line.

直线的参数方程为 $\begin{aligned}x&=2t+2 \\ y&=5-4t \\ z&=-t-3\end{aligned}$。求这直线的一个方向向量和直线上一点,并写出这条直线的向量方程。

习题 13 Exercise 13

Find the vector equation and parametric equations for the line through the two points $(4, 10, 0),$ $(1,-5,-6)$.

求经过 $(4, 10, 0),$ $(1,-5,-6)$ 两点的直线的向量方程与参数方程。

习题 14 Exercise 14

Find the point on the line segment from $P = (-4, 7, 5)$ to $Q = (2,-2,-3)$ which is $\frac{1}{7}$ of the way from $P$ to $Q$.

求从 $P = (-4, 7, 5)$ 到 $Q = (2,-2,-3)$ 的线段上、从 $P$ 到 $Q$ 走了 $\frac{1}{7}$ 路程的那个点。

习题 15 Exercise 15

Suppose a triangle in $\mathbb{R}^n$ has vertices at $P_1,$ $P_2,$ and $P_3$. Consider the lines which are drawn from a vertex to the mid point of the opposite side. Show these three lines intersect in a point and find the coordinates of this point.

设 $\mathbb{R}^n$ 中一个三角形的顶点为 $P_1,$ $P_2,$ 和 $P_3$。考虑从一个顶点到对边中点所画的直线。证明这三条直线交于一点,并求这点的坐标。

习题 16 Exercise 16

Find $\left[\begin{array}{c}1\\2\\3\\4\end{array}\right]\bullet\left[\begin{array}{c}2\\0\\1\\3\end{array}\right]$.

求 $\left[\begin{array}{c}1\\2\\3\\4\end{array}\right]\bullet\left[\begin{array}{c}2\\0\\1\\3\end{array}\right]$。

答案 Answer:$\left[\begin{array}{c}1\\2\\3\\4\end{array}\right]\bullet\left[\begin{array}{c}2\\0\\1\\3\end{array}\right]=17$

答案:$\left[\begin{array}{c}1\\2\\3\\4\end{array}\right]\bullet\left[\begin{array}{c}2\\0\\1\\3\end{array}\right]=17$

习题 17 Exercise 17

Use the formula given in Proposition 4.7.2 to verify the Cauchy Schwarz inequality and to show that equality occurs if and only if one of the vectors is a scalar multiple of the other.

用命题 4.7.2 中给出的公式验证柯西-施瓦茨不等式,并证明等号成立当且仅当一个向量是另一个向量的数倍。

答案 Answer:This formula says that $|\vec{u}\bullet\vec{v}| = \|\vec{u}\|\|\vec{v}\|\cos\theta$ where $\theta$ is the included angle between the two vectors. Thus $|\vec{u}\bullet\vec{v}|=\|\vec{u}\|\|\vec{v}\||\cos\theta|\leq \|\vec{u}\|\|\vec{v}\|$ and equality holds if and only if $\theta = 0$ or $\pi$. This means that the two vectors either point in the same direction or opposite directions. Hence one is a multiple of the other.

答案:该公式说 $|\vec{u}\bullet\vec{v}| = \|\vec{u}\|\|\vec{v}\|\cos\theta$,其中 $\theta$ 是两个向量的夹角。因此 $|\vec{u}\bullet\vec{v}|=\|\vec{u}\|\|\vec{v}\||\cos\theta|\leq \|\vec{u}\|\|\vec{v}\|$,且等号成立当且仅当 $\theta = 0$ 或 $\pi$。这意味着两向量要么同向要么反向,因而一个是另一个的倍数。

习题 18 Exercise 18

For $\vec{u}$, $\vec{v}$ vectors in $\mathbb{R}^3$, define the product, $\vec{u}\ast\vec{v} = u_1v_1 +2u_2v_2 +3u_3v_3$. Show the axioms for a dot product all hold for this product. Prove $|\vec{u}\ast\vec{v}|\leq (\vec{u}\ast\vec{u})^{1/2}(\vec{v}\ast\vec{v})^{1/2}$

对 $\mathbb{R}^3$ 中的向量 $\vec{u}$, $\vec{v}$,定义乘积 $\vec{u}\ast\vec{v} = u_1v_1 +2u_2v_2 +3u_3v_3$。证明点积的公理对这个乘积全都成立,并证明 $|\vec{u}\ast\vec{v}|\leq (\vec{u}\ast\vec{u})^{1/2}(\vec{v}\ast\vec{v})^{1/2}$。

答案 Answer:This follows from the Cauchy Schwarz inequality and the proof of Theorem 4.7.1 which only used the properties of the dot product. Since this new product has the same properties the Cauchy Schwarz inequality holds for it as well.

答案:这由柯西-施瓦茨不等式以及定理 4.7.1(它只用到了点积的性质)的证明推出。因为这个新乘积具有同样的性质,柯西-施瓦茨不等式对它也成立。

习题 19 Exercise 19

Let $\vec{a}$, $\vec{b}$ be vectors. Show that $\left(\vec{a}\bullet\vec{b}\right)=\frac{1}{4}\left(\|\vec{a}+\vec{b}\|^2-\|\vec{a}-\vec{b}\|^2\right).$

设 $\vec{a}$, $\vec{b}$ 是向量。证明 $\left(\vec{a}\bullet\vec{b}\right)=\frac{1}{4}\left(\|\vec{a}+\vec{b}\|^2-\|\vec{a}-\vec{b}\|^2\right).$

习题 20 Exercise 20

Using the axioms of the dot product, prove the parallelogram identity: $\|\vec{a}+\vec{b}\|^2+\|\vec{a}-\vec{b}\|^2=2\|\vec{a}\|^2+2\|\vec{b}\|^2$

利用点积的公理,证明平行四边形恒等式:$\|\vec{a}+\vec{b}\|^2+\|\vec{a}-\vec{b}\|^2=2\|\vec{a}\|^2+2\|\vec{b}\|^2$。

习题 21 Exercise 21

Let $A$ be a real $m\times n$ matrix and let $\vec{u} \in \mathbb{R}^n$ and $\vec{v} \in \mathbb{R}^m$. Show $A\vec{u}\bullet\vec{v} =\vec{u}\bullet A^T\vec{v}$. Hint: Use the definition of matrix multiplication to do this.

设 $A$ 是 $m\times n$ 实矩阵,$\vec{u} \in \mathbb{R}^n$,$\vec{v} \in \mathbb{R}^m$。证明 $A\vec{u}\bullet\vec{v} =\vec{u}\bullet A^T\vec{v}$。提示:用矩阵乘法的定义来做。

答案 Answer:$A\vec{x}\bullet\vec{y}=\sum_k(A\vec{x})_ky_k=\sum_k\sum_iA_{ki}x_iy_k=\sum_i\sum_kA^T_{ik}x_iy_k=\vec{x}\bullet A^T\vec{y}$

答案:$A\vec{x}\bullet\vec{y}=\sum_k(A\vec{x})_ky_k=\sum_k\sum_iA_{ki}x_iy_k=\sum_i\sum_kA^T_{ik}x_iy_k=\vec{x}\bullet A^T\vec{y}$

习题 22 Exercise 22

Use the result of Problem 21 to verify directly that $(AB)^T = B^TA^T$ without making any reference to subscripts.

用习题 21 的结果直接验证 $(AB)^T = B^TA^T$,而不引用下标。

答案 Answer:$\begin{aligned}AB\vec{x}\bullet\vec{y}&=B\vec{x}\bullet A^T\vec{y} \\ &=\vec{x}\bullet B^TA^T\vec{y} \\ &=\vec{x}\bullet (AB)^T\vec{y}\end{aligned}$ Since this is true for all $\vec{x}$, it follows that, in particular, it holds for $\vec{x}=B^TA^T\vec{y}-(AB)^T\vec{y}$ and so from the axioms of the dot product, $\left(B^TA^T\vec{y}-(AB)^T\vec{y}\right)\bullet\left(B^TA^T\vec{y}-(AB)^T\vec{y}\right)=0$ and so $B^TA^T\vec{y}-(AB)^T\vec{y}=\vec{0}$. However, this is true for all $\vec{y}$ and so $B^TA^T-(AB)^T=0$.

答案:$\begin{aligned}AB\vec{x}\bullet\vec{y}&=B\vec{x}\bullet A^T\vec{y} \\ &=\vec{x}\bullet B^TA^T\vec{y} \\ &=\vec{x}\bullet (AB)^T\vec{y}\end{aligned}$ 由于这对一切 $\vec{x}$ 成立,特别地对 $\vec{x}=B^TA^T\vec{y}-(AB)^T\vec{y}$ 也成立,于是由点积公理有 $\left(B^TA^T\vec{y}-(AB)^T\vec{y}\right)\bullet\left(B^TA^T\vec{y}-(AB)^T\vec{y}\right)=0$,从而 $B^TA^T\vec{y}-(AB)^T\vec{y}=\vec{0}$。而这对一切 $\vec{y}$ 都成立,故 $B^TA^T-(AB)^T=0$。

习题 23 Exercise 23

Find the angle between the vectors $\vec{u}=\left[\begin{array}{r}3\\-1\\-1\end{array}\right],$ $\vec{v}=\left[\begin{array}{c}1\\4\\2\end{array}\right]$

求向量 $\vec{u}=\left[\begin{array}{r}3\\-1\\-1\end{array}\right]$ 与 $\vec{v}=\left[\begin{array}{c}1\\4\\2\end{array}\right]$ 之间的夹角。

答案 Answer:$\frac{\left[\begin{array}{ccc}3&-1&-1\end{array}\right]^T\bullet\left[\begin{array}{ccc}1&4&2\end{array}\right]^T}{\sqrt{9+1+1}\sqrt{1+16+4}}=\frac{-3}{\sqrt{11}\sqrt{21}}=-0.19739=\cos\theta$ Therefore we need to solve $-0.19739=\cos\theta$ Thus $\theta=1.7695$ radians.

答案:$\frac{\left[\begin{array}{ccc}3&-1&-1\end{array}\right]^T\bullet\left[\begin{array}{ccc}1&4&2\end{array}\right]^T}{\sqrt{9+1+1}\sqrt{1+16+4}}=\frac{-3}{\sqrt{11}\sqrt{21}}=-0.19739=\cos\theta$。因此需要解 $-0.19739=\cos\theta$,得 $\theta=1.7695$ 弧度。

习题 24 Exercise 24

Find the angle between the vectors $\vec{u}=\left[\begin{array}{r}1\\-2\\1\end{array}\right],$ $\vec{v}=\left[\begin{array}{r}1\\2\\-7\end{array}\right]$

求向量 $\vec{u}=\left[\begin{array}{r}1\\-2\\1\end{array}\right]$ 与 $\vec{v}=\left[\begin{array}{r}1\\2\\-7\end{array}\right]$ 之间的夹角。

答案 Answer:$\frac{-10}{\sqrt{1+4+1}\sqrt{1+4+49}}=-0.55555=\cos\theta$ Therefore we need to solve $-0.55555 = \cos \theta$, which gives $\theta = 2.0313$ radians.

答案:$\frac{-10}{\sqrt{1+4+1}\sqrt{1+4+49}}=-0.55555=\cos\theta$。因此需要解 $-0.55555 = \cos \theta$,得 $\theta = 2.0313$ 弧度。

习题 25 Exercise 25

Find $\text{proj}_{\vec{v}}(\vec{w})$ where $\vec{w}=\left[\begin{array}{r}1\\0\\-2\end{array}\right]$ and $\vec{v}=\left[\begin{array}{c}1\\2\\3\end{array}\right]$.

求 $\text{proj}_{\vec{v}}(\vec{w})$,其中 $\vec{w}=\left[\begin{array}{r}1\\0\\-2\end{array}\right]$,$\vec{v}=\left[\begin{array}{c}1\\2\\3\end{array}\right]$。

答案 Answer:$\frac{\vec{u}\bullet\vec{v}}{\vec{u}\bullet\vec{u}}\vec{u}=\frac{-5}{14}\left[\begin{array}{c}1\\2\\3\end{array}\right]=\left[\begin{array}{r}-\frac{5}{14}\\-\frac{5}{7}\\-\frac{15}{14}\end{array}\right]$

答案:$\frac{\vec{u}\bullet\vec{v}}{\vec{u}\bullet\vec{u}}\vec{u}=\frac{-5}{14}\left[\begin{array}{c}1\\2\\3\end{array}\right]=\left[\begin{array}{r}-\frac{5}{14}\\-\frac{5}{7}\\-\frac{15}{14}\end{array}\right]$

习题 26 Exercise 26

Find $\text{proj}_{\vec{v}}(\vec{w})$ where $\vec{w}=\left[\begin{array}{r}1\\2\\-2\end{array}\right]$ and $\vec{v}=\left[\begin{array}{c}1\\0\\3\end{array}\right]$.

求 $\text{proj}_{\vec{v}}(\vec{w})$,其中 $\vec{w}=\left[\begin{array}{r}1\\2\\-2\end{array}\right]$,$\vec{v}=\left[\begin{array}{c}1\\0\\3\end{array}\right]$。

答案 Answer:$\frac{\vec{u}\bullet\vec{v}}{\vec{u}\bullet\vec{u}}\vec{u}=\frac{-5}{10}\left[\begin{array}{c}1\\0\\3\end{array}\right]=\left[\begin{array}{r}-\frac{1}{2}\\0\\-\frac{3}{2}\end{array}\right]$

答案:$\frac{\vec{u}\bullet\vec{v}}{\vec{u}\bullet\vec{u}}\vec{u}=\frac{-5}{10}\left[\begin{array}{c}1\\0\\3\end{array}\right]=\left[\begin{array}{r}-\frac{1}{2}\\0\\-\frac{3}{2}\end{array}\right]$

习题 27 Exercise 27

Find $\text{proj}_{\vec{v}}(\vec{w})$ where $\vec{w}=\left[\begin{array}{r}1\\2\\-2\\1\end{array}\right]$ and $\vec{v}=\left[\begin{array}{c}1\\2\\3\\0\end{array}\right]$.

求 $\text{proj}_{\vec{v}}(\vec{w})$,其中 $\vec{w}=\left[\begin{array}{r}1\\2\\-2\\1\end{array}\right]$,$\vec{v}=\left[\begin{array}{c}1\\2\\3\\0\end{array}\right]$。

答案 Answer:$\frac{\vec{u}\bullet\vec{v}}{\vec{u}\bullet\vec{u}}\vec{u}=\frac{\left[\begin{array}{cccc}1&2&-2&1\end{array}\right]^T\bullet\left[\begin{array}{cccc}1&2&3&0\end{array}\right]^T}{1+4+9}\left[\begin{array}{c}1\\2\\3\\0\end{array}\right]=\left[\begin{array}{r}-\frac{1}{14}\\-\frac{1}{7}\\-\frac{3}{14}\\0\end{array}\right]$

答案:$\frac{\vec{u}\bullet\vec{v}}{\vec{u}\bullet\vec{u}}\vec{u}=\frac{\left[\begin{array}{cccc}1&2&-2&1\end{array}\right]^T\bullet\left[\begin{array}{cccc}1&2&3&0\end{array}\right]^T}{1+4+9}\left[\begin{array}{c}1\\2\\3\\0\end{array}\right]=\left[\begin{array}{r}-\frac{1}{14}\\-\frac{1}{7}\\-\frac{3}{14}\\0\end{array}\right]$

习题 28 Exercise 28

Let $P = (1, 2, 3)$ be a point in $\mathbb{R}^3$. Let $L$ be the line through the point $P_0 = (1, 4, 5)$ with direction vector $\vec{d} =\left[\begin{array}{r}1\\-1\\1\end{array}\right]$. Find the shortest distance from $P$ to $L$, and find the point $Q$ on $L$ that is closest to $P$.

设 $P = (1, 2, 3)$ 是 $\mathbb{R}^3$ 中的一点。设 $L$ 是经过点 $P_0 = (1, 4, 5)$、方向向量为 $\vec{d} =\left[\begin{array}{r}1\\-1\\1\end{array}\right]$ 的直线。求 $P$ 到 $L$ 的最短距离,并求 $L$ 上离 $P$ 最近的点 $Q$。

习题 29 Exercise 29

Let $P = (0, 2, 1)$ be a point in $\mathbb{R}^3$. Let $L$ be the line through the point $P_0 = (1, 1, 1)$ with direction vector $\vec{d} =\left[\begin{array}{c}3\\0\\1\end{array}\right]$. Find the shortest distance from $P$ to $L$, and find the point $Q$ on $L$ that is closest to $P$.

设 $P = (0, 2, 1)$ 是 $\mathbb{R}^3$ 中的一点。设 $L$ 是经过点 $P_0 = (1, 1, 1)$、方向向量为 $\vec{d} =\left[\begin{array}{c}3\\0\\1\end{array}\right]$ 的直线。求 $P$ 到 $L$ 的最短距离,并求 $L$ 上离 $P$ 最近的点 $Q$。

习题 30 Exercise 30

Does it make sense to speak of $\text{proj}_{\vec{0}} (\vec{w})$?

谈 $\text{proj}_{\vec{0}} (\vec{w})$ 有意义吗?

答案 Answer:No, it does not. The $0$ vector has no direction. The formula for $\text{proj}_{\vec{0}} (\vec{w})$ doesn't make sense either.

答案:没有意义。$0$ 向量没有方向,$\text{proj}_{\vec{0}} (\vec{w})$ 的公式也说不通。

习题 31 Exercise 31

Prove the Cauchy Schwarz inequality in $\mathbb{R}^n$ as follows. For $\vec{u},$ $\vec{v}$ vectors, consider $(\vec{w}-\text{proj}_{\vec{v}}\vec{w})\bullet (\vec{w}-\text{proj}_{\vec{v}}\vec{w})\geq 0$ Simplify using the axioms of the dot product and then put in the formula for the projection. Notice that this expression equals $0$ and you get equality in the Cauchy Schwarz inequality if and only if $\vec{w} = \text{proj}_{\vec{v}}\vec{w}$. What is the geometric meaning of $\vec{w}= \text{proj}_{\vec{v}}\vec{w}$?

像下面这样证明 $\mathbb{R}^n$ 中的柯西-施瓦茨不等式。对向量 $\vec{u},$ $\vec{v}$,考虑 $(\vec{w}-\text{proj}_{\vec{v}}\vec{w})\bullet (\vec{w}-\text{proj}_{\vec{v}}\vec{w})\geq 0$。用点积的公理化简,然后代入投影公式。注意:此式当且仅当 $\vec{w} = \text{proj}_{\vec{v}}\vec{w}$ 时等于 $0$,此时柯西-施瓦茨不等式取等号。$\vec{w}= \text{proj}_{\vec{v}}\vec{w}$ 的几何意义是什么?

答案 Answer:$\left(\vec{u}-\frac{\vec{u}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v}\right)\bullet\left(\vec{u}-\frac{\vec{u}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v}\right)=\|\vec{u}\|^2-2(\vec{u}\bullet\vec{v})^2\frac{1}{\|\vec{v}\|^2}+(\vec{u}\bullet\vec{v})^2\frac{1}{\|\vec{v}\|^2}\geq 0$ And so $\|\vec{u}\|^2\|\vec{v}\|^2\geq (\vec{u}\bullet\vec{v})^2$ You get equality exactly when $\vec{u}=\text{proj}_{\vec{v}}\vec{u}=\frac{\vec{u}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v}$ in other words, when $\vec{u}$ is a multiple of $\vec{v}$.

答案:$\left(\vec{u}-\frac{\vec{u}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v}\right)\bullet\left(\vec{u}-\frac{\vec{u}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v}\right)=\|\vec{u}\|^2-2(\vec{u}\bullet\vec{v})^2\frac{1}{\|\vec{v}\|^2}+(\vec{u}\bullet\vec{v})^2\frac{1}{\|\vec{v}\|^2}\geq 0$,于是 $\|\vec{u}\|^2\|\vec{v}\|^2\geq (\vec{u}\bullet\vec{v})^2$。当 $\vec{u}=\text{proj}_{\vec{v}}\vec{u}=\frac{\vec{u}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v}$,即 $\vec{u}$ 是 $\vec{v}$ 的倍数时,恰好取等号。

习题 32 Exercise 32

Let $\vec{v},$ $\vec{w},$ $\vec{u}$ be vectors. Show that $(\vec{w}+\vec{u})_{\perp}=\vec{w}_{\perp} +\vec{u}_{\perp}$ where $\vec{w}_{\perp} =\vec{w}-\text{proj}_{\vec{v}}(\vec{w})$.

设 $\vec{v},$ $\vec{w},$ $\vec{u}$ 是向量。证明 $(\vec{w}+\vec{u})_{\perp}=\vec{w}_{\perp} +\vec{u}_{\perp}$,其中 $\vec{w}_{\perp} =\vec{w}-\text{proj}_{\vec{v}}(\vec{w})$。

答案 Answer:$\begin{aligned}\vec{w}-\text{proj}_{\vec{v}}(\vec{w})+\vec{u}-\text{proj}_{\vec{v}}(\vec{u})&=\vec{w}+\vec{u}-(\text{proj}_{\vec{v}}(\vec{w})+\text{proj}_{\vec{v}}(\vec{u})) \\ &=\vec{w}+\vec{u}-\text{proj}_{\vec{v}}(\vec{w}+\vec{u})\end{aligned}$ This follows because $\begin{aligned}\text{proj}_{\vec{v}}(\vec{w})+\text{proj}_{\vec{v}}(\vec{u})&=\frac{\vec{u}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v}+\frac{\vec{w}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v} \\ &=\frac{(\vec{u}+\vec{w})\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v} \\ &=\text{proj}_{\vec{v}}(\vec{w}+\vec{u})\end{aligned}$

答案:$\begin{aligned}\vec{w}-\text{proj}_{\vec{v}}(\vec{w})+\vec{u}-\text{proj}_{\vec{v}}(\vec{u})&=\vec{w}+\vec{u}-(\text{proj}_{\vec{v}}(\vec{w})+\text{proj}_{\vec{v}}(\vec{u})) \\ &=\vec{w}+\vec{u}-\text{proj}_{\vec{v}}(\vec{w}+\vec{u})\end{aligned}$ 这是因为 $\begin{aligned}\text{proj}_{\vec{v}}(\vec{w})+\text{proj}_{\vec{v}}(\vec{u})&=\frac{\vec{u}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v}+\frac{\vec{w}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v} \\ &=\frac{(\vec{u}+\vec{w})\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v} \\ &=\text{proj}_{\vec{v}}(\vec{w}+\vec{u})\end{aligned}$。

习题 33 Exercise 33

Show that $(\vec{v}-\text{proj}_{\vec{u}}(\vec{v}),\vec{u})=(\vec{v}-\text{proj}_{\vec{u}}(\vec{v}))\bullet\vec{u}=0$ and conclude every vector in $\mathbb{R}^n$ can be written as the sum of two vectors, one which is perpendicular and one which is parallel to the given vector.

证明 $(\vec{v}-\text{proj}_{\vec{u}}(\vec{v}))\bullet\vec{u}=0$,并由此得出 $\mathbb{R}^n$ 中每个向量都可写成两个向量之和,其中一个垂直于已知向量,一个平行于已知向量。

答案 Answer:$(\vec{v}-\text{proj}_{\vec{u}}(\vec{v}))\bullet\vec{u}=\vec{v}\bullet\vec{u}-\left(\frac{(\vec{v}\cdot\vec{u}}{\|\vec{u}\|^2}\vec{u}\right)\bullet\vec{u}=\vec{v}\bullet\vec{u}-\vec{v}\bullet\vec{u}=0$. Therefore, $\vec{v}=\vec{v}-\text{proj}_{\vec{u}}(\vec{v})+\text{proj}_{\vec{u}}(\vec{v})$. The first is perpendicular to $\vec{u}$ and the second is a multiple of $\vec{u}$ so it is parallel to $\vec{u}$.

答案:$(\vec{v}-\text{proj}_{\vec{u}}(\vec{v}))\bullet\vec{u}=\vec{v}\bullet\vec{u}-\left(\frac{(\vec{v}\cdot\vec{u}}{\|\vec{u}\|^2}\vec{u}\right)\bullet\vec{u}=\vec{v}\bullet\vec{u}-\vec{v}\bullet\vec{u}=0$。因此 $\vec{v}=\vec{v}-\text{proj}_{\vec{u}}(\vec{v})+\text{proj}_{\vec{u}}(\vec{v})$。第一个垂直于 $\vec{u}$,第二个是 $\vec{u}$ 的倍数,因而平行于 $\vec{u}$。

习题 34 Exercise 34

Show that if $\vec{a}\times\vec{u}=\vec{0}$ for any unit vector $\vec{u}$, then $\vec{a}=\vec{0}$.

证明:如果对任何单位向量 $\vec{u}$ 都有 $\vec{a}\times\vec{u}=\vec{0}$,则 $\vec{a}=\vec{0}$。

答案 Answer:If $\vec{a}\neq\vec{0}$, then the condition says that $\|\vec{a}\times\vec{u}\|=\|\vec{a}\|\sin\theta =0$ for all angles $\theta$. Hence $\vec{a}=\vec{0}$ after all.

答案:若 $\vec{a}\neq\vec{0}$,则条件说明对所有角度 $\theta$ 都有 $\|\vec{a}\times\vec{u}\|=\|\vec{a}\|\sin\theta =0$。因此终究 $\vec{a}=\vec{0}$。

习题 35 Exercise 35

Find the area of the triangle determined by the three points $(1, 2, 3),$ $(4, 2, 0)$ and $(-3, 2, 1)$.

求由 $(1, 2, 3),$ $(4, 2, 0)$ 与 $(-3, 2, 1)$ 三点确定的三角形的面积。

答案 Answer:$\left[\begin{array}{r}3\\0\\-3\end{array}\right]\times\left[\begin{array}{r}-4\\0\\-2\end{array}\right]=\left[\begin{array}{r}0\\18\\0\end{array}\right]$. So the area is $9$.

答案:$\left[\begin{array}{r}3\\0\\-3\end{array}\right]\times\left[\begin{array}{r}-4\\0\\-2\end{array}\right]=\left[\begin{array}{r}0\\18\\0\end{array}\right]$。所以面积为 $9$。

习题 36 Exercise 36

Find the area of the triangle determined by the three points $(1, 0, 3),$ $(4, 1, 0)$ and $(-3, 1, 1)$.

求由 $(1, 0, 3),$ $(4, 1, 0)$ 与 $(-3, 1, 1)$ 三点确定的三角形的面积。

答案 Answer:$\left[\begin{array}{r}3\\1\\-3\end{array}\right]\times\left[\begin{array}{r}-4\\1\\-2\end{array}\right]=\left[\begin{array}{c}1\\18\\7\end{array}\right]$. The area is given by $\frac{1}{2}\sqrt{1+(18)^2+49}=\frac{1}{2}\sqrt{374}$

答案:$\left[\begin{array}{r}3\\1\\-3\end{array}\right]\times\left[\begin{array}{r}-4\\1\\-2\end{array}\right]=\left[\begin{array}{c}1\\18\\7\end{array}\right]$。面积为 $\frac{1}{2}\sqrt{1+(18)^2+49}=\frac{1}{2}\sqrt{374}$。

习题 37 Exercise 37

Find the area of the triangle determined by the three points, $(1, 2, 3),$ $(2, 3, 4)$ and $(3, 4, 5)$. Did something interesting happen here? What does it mean geometrically?

求由 $(1, 2, 3),$ $(2, 3, 4)$ 与 $(3, 4, 5)$ 三点确定的三角形的面积。这里发生了什么有趣的事?它的几何意义是什么?

答案 Answer:$\left[\begin{array}{ccc}1&1&1\end{array}\right]\times\left[\begin{array}{ccc}2&2&2\end{array}\right]=\left[\begin{array}{ccc}0&0&0\end{array}\right]$. The area is $0$. It means the three points are on the same line.

答案:$\left[\begin{array}{ccc}1&1&1\end{array}\right]\times\left[\begin{array}{ccc}2&2&2\end{array}\right]=\left[\begin{array}{ccc}0&0&0\end{array}\right]$。面积为 $0$,意味着三点在一条直线上。

习题 38 Exercise 38

Find the area of the parallelogram determined by the vectors $\left[\begin{array}{c}1\\2\\3\end{array}\right]$, $\left[\begin{array}{r}3\\-2\\1\end{array}\right]$.

求由向量 $\left[\begin{array}{c}1\\2\\3\end{array}\right]$ 与 $\left[\begin{array}{r}3\\-2\\1\end{array}\right]$ 确定的平行四边形的面积。

答案 Answer:$\left[\begin{array}{c}1\\2\\3\end{array}\right]\times\left[\begin{array}{r}3\\-2\\1\end{array}\right]=\left[\begin{array}{r}8\\8\\-8\end{array}\right]$. The area is $8\sqrt{3}$.

答案:$\left[\begin{array}{c}1\\2\\3\end{array}\right]\times\left[\begin{array}{r}3\\-2\\1\end{array}\right]=\left[\begin{array}{r}8\\8\\-8\end{array}\right]$。面积为 $8\sqrt{3}$。

习题 39 Exercise 39

Find the area of the parallelogram determined by the vectors $\left[\begin{array}{c}1\\0\\3\end{array}\right]$, $\left[\begin{array}{r}4\\-2\\1\end{array}\right]$.

求由向量 $\left[\begin{array}{c}1\\0\\3\end{array}\right]$ 与 $\left[\begin{array}{r}4\\-2\\1\end{array}\right]$ 确定的平行四边形的面积。

答案 Answer:$\left[\begin{array}{c}1\\0\\3\end{array}\right]\times\left[\begin{array}{r}4\\-2\\1\end{array}\right]=\left[\begin{array}{r}6\\11\\-2\end{array}\right]$. The area is $\sqrt{36+121+4}=\sqrt{161}$.

答案:$\left[\begin{array}{c}1\\0\\3\end{array}\right]\times\left[\begin{array}{r}4\\-2\\1\end{array}\right]=\left[\begin{array}{r}6\\11\\-2\end{array}\right]$。面积为 $\sqrt{36+121+4}=\sqrt{161}$。

习题 40 Exercise 40

Is $\vec{u}\times (\vec{v}\times\vec{w})=(\vec{u}\times\vec{v})\times\vec{w}$? What is the meaning of $\vec{u}\times\vec{v}\times\vec{w}$? Explain. Hint: Try $\left(\vec{i}\times\vec{j}\right)\times\vec{k}$.

$\vec{u}\times (\vec{v}\times\vec{w})=(\vec{u}\times\vec{v})\times\vec{w}$ 是否成立?$\vec{u}\times\vec{v}\times\vec{w}$ 是什么意思?请解释。提示:试试 $\left(\vec{i}\times\vec{j}\right)\times\vec{k}$。

答案 Answer:$\left(\vec{i}\times\vec{j}\right)\times\vec{j}=\vec{k}\times\vec{j}=i\vec{i}$. However, $\vec{i}\times\left(\vec{j}\times\vec{j}\right)=\vec{0}$ and so the cross product is not associative.

答案:$\left(\vec{i}\times\vec{j}\right)\times\vec{j}=\vec{k}\times\vec{j}=i\vec{i}$。但 $\vec{i}\times\left(\vec{j}\times\vec{j}\right)=\vec{0}$,所以叉积不满足结合律。

习题 41 Exercise 41

Verify directly that the coordinate description of the cross product, $\vec{u}\times\vec{v}$ has the property that it is perpendicular to both $\vec{u}$ and $\vec{v}$. Then show by direct computation that this coordinate description satisfies $\begin{aligned} \|\vec{u}\times\vec{v}\|^2&=\|\vec{u}\|^2\|\vec{v}\|^2-(\vec{u}\bullet\vec{v})^2 \\ &=\|\vec{u}\|^2\|\vec{v}\|^2(1-\cos^2(\theta ))\end{aligned}$ where $\theta$ is the angle included between the two vectors. Explain why $\|\vec{u}\times\vec{v}\|$ has the correct magnitude.

直接验证叉积的坐标描述 $\vec{u}\times\vec{v}$ 具有既垂直于 $\vec{u}$ 又垂直于 $\vec{v}$ 的性质。然后直接计算证明这一坐标描述满足 $\begin{aligned} \|\vec{u}\times\vec{v}\|^2&=\|\vec{u}\|^2\|\vec{v}\|^2-(\vec{u}\bullet\vec{v})^2 \\ &=\|\vec{u}\|^2\|\vec{v}\|^2(1-\cos^2(\theta ))\end{aligned}$,其中 $\theta$ 是两向量的夹角。解释为什么 $\|\vec{u}\times\vec{v}\|$ 具有正确的大小。

答案 Answer:Verify directly from the coordinate description of the cross product that the right hand rule applies to the vectors $\vec{i},\vec{j},\vec{k}$. Next verify that the distributive law holds for the coordinate description of the cross product. This gives another way to approach the cross product. First define it in terms of coordinates and then get the geometric properties from this. However, this approach does not yield the right hand rule property very easily. From the coordinate description, $\vec{a}\times\vec{b}\cdot\vec{a}=\epsilon_{ijk}a_jb_ka_i=-\epsilon_{jik}a_kb_ka_i=-\epsilon_{jik}b_ka_ia_j=-\vec{a}\times\vec{b}\cdot\vec{a}$ and so $\vec{a}\times\vec{b}$ is perpendicular to $\vec{a}$. Similarly, $\vec{a}\times\vec{b}$ is perpendicular to $\vec{b}$. Now we need that $\|\vec{a}\times\vec{b}\|^2=\|\vec{a}\|^2\|\vec{b}\|^2(1-\cos^2\theta )=\|\vec{a}\|^2\|\vec{b}\|^2\sin^2\theta$ and so $\|\vec{a}\times\vec{b}\|=\|\vec{a}\|\|\vec{b}\|\sin\theta$, the area of the parallelogram determined by $\vec{a}$, $\vec{b}$. Only the right hand rule is a little problematic. However, you can see right away from the component definition that the right hand rule holds for each of the standard unit vectors. Thus $\vec{i}\times\vec{j}=\vec{k}$ etc. $\left\|\begin{array}{ccc}\vec{i}&\vec{j}&\vec{k}\\1&0&0\\0&1&0\end{array}\right\|=\vec{k}$

答案:直接从叉积的坐标描述验证右手定则适用于向量 $\vec{i},\vec{j},\vec{k}$。再验证分配律对叉积的坐标描述成立。这给出了研究叉积的另一种途径:先用坐标定义,再由此得出几何性质。不过这方法不太容易导出右手定则这一性质。由坐标描述,$\vec{a}\times\vec{b}\cdot\vec{a}=\epsilon_{ijk}a_jb_ka_i=-\epsilon_{jik}a_kb_ka_i=-\epsilon_{jik}b_ka_ia_j=-\vec{a}\times\vec{b}\cdot\vec{a}$,所以 $\vec{a}\times\vec{b}$ 垂直于 $\vec{a}$。类似地,$\vec{a}\times\vec{b}$ 垂直于 $\vec{b}$。还需 $\|\vec{a}\times\vec{b}\|^2=\|\vec{a}\|^2\|\vec{b}\|^2(1-\cos^2\theta )=\|\vec{a}\|^2\|\vec{b}\|^2\sin^2\theta$,从而 $\|\vec{a}\times\vec{b}\|=\|\vec{a}\|\|\vec{b}\|\sin\theta$,即由 $\vec{a}$, $\vec{b}$ 确定的平行四边形面积。只有右手定则稍难些。不过由分量定义立刻可看出右手定则对每个标准单位向量都成立。于是 $\vec{i}\times\vec{j}=\vec{k}$ 等等。$\left\|\begin{array}{ccc}\vec{i}&\vec{j}&\vec{k}\\1&0&0\\0&1&0\end{array}\right\|=\vec{k}$。

习题 42 Exercise 42

Suppose $A$ is a $3\times 3$ skew symmetric matrix such that $A^T = -A$. Show there exists a vector $\vec{\Omega}$ such that for all $\vec{u} \in \mathbb{R}^3$ $A\vec{u}=\vec{\Omega}\times\vec{u}$ Hint: Explain why since $A$ is skew symmetric it is of the form $A=\left[\begin{array}{ccc}0&-\omega_3&\omega_2 \\ \omega_3&0&-\omega_1 \\ -\omega_2&\omega_1&0\end{array}\right]$ where the $\omega_i$ are numbers. Then consider $\omega_1\vec{i}+\omega_2\vec{j}+\omega_3\vec{k}$.

设 $A$ 是满足 $A^T = -A$ 的 $3\times 3$ 反对称矩阵。证明存在一个向量 $\vec{\Omega}$,使得对所有 $\vec{u} \in \mathbb{R}^3$ 都有 $A\vec{u}=\vec{\Omega}\times\vec{u}$。提示:说明由于 $A$ 反对称,它的形如 $A=\left[\begin{array}{ccc}0&-\omega_3&\omega_2 \\ \omega_3&0&-\omega_1 \\ -\omega_2&\omega_1&0\end{array}\right]$,其中 $\omega_i$ 是数。再考虑 $\omega_1\vec{i}+\omega_2\vec{j}+\omega_3\vec{k}$。

习题 43 Exercise 43

Find the volume of the parallelepiped determined by the vectors $\left[\begin{array}{r}1\\-7\\-5\end{array}\right]$, $\left[\begin{array}{r}1\\-2\\-6\end{array}\right]$, and $\left[\begin{array}{c}3\\2\\3\end{array}\right]$.

求由向量 $\left[\begin{array}{r}1\\-7\\-5\end{array}\right]$、$\left[\begin{array}{r}1\\-2\\-6\end{array}\right]$ 与 $\left[\begin{array}{c}3\\2\\3\end{array}\right]$ 确定的平行六面体的体积。

答案 Answer:$\left\|\begin{array}{ccc}1&-7&-5 \\ 1&-2&-6 \\ 3&2&3\end{array}\right\|=113$

答案:$\left\|\begin{array}{ccc}1&-7&-5 \\ 1&-2&-6 \\ 3&2&3\end{array}\right\|=113$

习题 44 Exercise 44

Suppose $\vec{u}$, $\vec{v}$, and $\vec{w}$ are three vectors whose components are all integers. Can you conclude the volume of the parallelepiped determined from these three vectors will always be an integer?

设 $\vec{u}$, $\vec{v}$, $\vec{w}$ 是三个分量均为整数的向量。你能断定由这三个向量确定的平行六面体的体积总是整数吗?

答案 Answer:Yes. It will involve the sum of product of integers and so it will be an integer.

答案:能。它会涉及整数乘积的和,因而是整数。

习题 45 Exercise 45

What does it mean geometrically if the box product of three vectors gives zero?

若三个向量的混合积为 $0$,它在几何上意味着什么?

答案 Answer:It means that if you place them so that they all have their tails at the same point, the three will lie in the same plane.

答案:它意味着,若把它们放置得使它们的起点在同一点,则这三个向量位于同一平面内。

习题 46 Exercise 46

Using Problem 45, find an equation of a plane containing the two position vectors, $\vec{p}$ and $\vec{q}$ and the point $0$. Hint: If $(x, y,z)$ is a point on this plane, the volume of the parallelepiped determined by $(x, y,z)$ and the vectors $\vec{p}$, $\vec{q}$ equals $0$.

利用习题 45,求一个包含两个位置向量 $\vec{p}$、$\vec{q}$ 以及点 $0$ 的平面的方程。提示:若 $(x, y,z)$ 是该平面上一点,则由 $(x, y,z)$ 与向量 $\vec{p}$、$\vec{q}$ 确定的平行六面体的体积等于 $0$。

答案 Answer:$\vec{x}\bullet\left(\vec{a}\times\vec{b}\right)=0$

答案:$\vec{x}\bullet\left(\vec{a}\times\vec{b}\right)=0$

习题 47 Exercise 47

Using the notion of the box product yielding either plus or minus the volume of the parallelepiped determined by the given three vectors, show that $(\vec{u}\times\vec{v})\bullet\vec{w}=\vec{u}\bullet (\vec{v}\times\vec{w})$ In other words, the dot and the cross can be switched as long as the order of the vectors remains the same. Hint: There are two ways to do this, by the coordinate description of the dot and cross product and by geometric reasoning.

利用混合积给出给定三向量确定的平行六面体体积的正负这一概念,证明 $(\vec{u}\times\vec{v})\bullet\vec{w}=\vec{u}\bullet (\vec{v}\times\vec{w})$。换言之,只要向量的顺序不变,点与叉可以交换。提示:有两种做法,一是用点积与叉积的坐标描述,二是用几何推理。

习题 48 Exercise 48

Simplify $(\vec{u}\times\vec{v})\bullet [(\vec{v}\times\vec{w})\times (\vec{w}\times\vec{z})]$.

化简 $(\vec{u}\times\vec{v})\bullet [(\vec{v}\times\vec{w})\times (\vec{w}\times\vec{z})]$。

答案 Answer:Here $[\vec{v},\vec{w},\vec{z}]$ denotes the box product. Consider the cross product term. From the above, $\begin{aligned}(\vec{v}\times\vec{w})\times(\vec{w}\times\vec{z})&=[\vec{v},\vec{w},\vec{z}]\vec{w}-[\vec{w},\vec{w},\vec{z}]\vec{v} \\ &=[\vec{v},\vec{w},\vec{z}]\vec{w}\end{aligned}$ Thus it reduces to $(\vec{u}\times\vec{v})\bullet [\vec{v},\vec{w},\vec{z}]\vec{w}=[\vec{v},\vec{w},\vec{z}][\vec{u},\vec{v},\vec{w}]$

答案:这里 $[\vec{v},\vec{w},\vec{z}]$ 表示混合积。考虑叉积项。由上面,$\begin{aligned}(\vec{v}\times\vec{w})\times(\vec{w}\times\vec{z})&=[\vec{v},\vec{w},\vec{z}]\vec{w}-[\vec{w},\vec{w},\vec{z}]\vec{v} \\ &=[\vec{v},\vec{w},\vec{z}]\vec{w}\end{aligned}$。于是化简为 $(\vec{u}\times\vec{v})\bullet [\vec{v},\vec{w},\vec{z}]\vec{w}=[\vec{v},\vec{w},\vec{z}][\vec{u},\vec{v},\vec{w}]$。

习题 49 Exercise 49

Simplify $\|\vec{u}\times\vec{v}\|^2+(\vec{u}\bullet\vec{v})^2-\|\vec{u}\|^2\|\vec{v}\|^2$.

化简 $\|\vec{u}\times\vec{v}\|^2+(\vec{u}\bullet\vec{v})^2-\|\vec{u}\|^2\|\vec{v}\|^2$。

答案 Answer:$\begin{aligned}\|\vec{u}\times\vec{v}\|^2&=\epsilon_{ijk}u_jv_k\epsilon_{irs}u_rv_s=(\delta_{jr}\delta_{ks}-\delta_{kr}\delta_{js})u_rv_su_jv_k \\ &=u_jv_ku_jv_k-u_kv_ju_jv_k=\|\vec{u}\|^2\|\vec{v}\|^2-(\vec{u}\bullet\vec{v})^2\end{aligned}$ It follows that the expression reduces to $0$. You can also do the following. $\begin{aligned}\|\vec{u}\times\vec{v}\|^2&=\|\vec{u}\|^2\|\vec{v}\|^2\sin^2\theta \\ &=\|\vec{u}\|^2\|\vec{v}\|^2(1-\cos^2\theta ) \\ &=\|\vec{u}\|^2\|\vec{v}\|^2-\|\vec{u}\|^2\|\vec{v}\|^2\cos^2\theta \\ &=\|\vec{u}\|^2\|\vec{v}\|^2-(\vec{u}\bullet\vec{v})^2\end{aligned}$ which implies the expression equals $0$.

答案:$\begin{aligned}\|\vec{u}\times\vec{v}\|^2&=\epsilon_{ijk}u_jv_k\epsilon_{irs}u_rv_s=(\delta_{jr}\delta_{ks}-\delta_{kr}\delta_{js})u_rv_su_jv_k \\ &=u_jv_ku_jv_k-u_kv_ju_jv_k=\|\vec{u}\|^2\|\vec{v}\|^2-(\vec{u}\bullet\vec{v})^2\end{aligned}$,因此原式化简为 $0$。也可以这样:$\begin{aligned}\|\vec{u}\times\vec{v}\|^2&=\|\vec{u}\|^2\|\vec{v}\|^2\sin^2\theta \\ &=\|\vec{u}\|^2\|\vec{v}\|^2(1-\cos^2\theta ) \\ &=\|\vec{u}\|^2\|\vec{v}\|^2-\|\vec{u}\|^2\|\vec{v}\|^2\cos^2\theta \\ &=\|\vec{u}\|^2\|\vec{v}\|^2-(\vec{u}\bullet\vec{v})^2\end{aligned}$,从而原式等于 $0$。

习题 50 Exercise 50

For $\vec{u},$ $\vec{v},$ $\vec{w}$ functions of $t$, prove the following product rules: $\begin{aligned}(\vec{u}\times\vec{v})'&=\vec{u}'\times\vec{v}+\vec{u}\times\vec{v}' \\ (\vec{u}\bullet\vec{v})'&=\vec{u}'\bullet\vec{v}+\vec{u}\bullet\vec{v}'\end{aligned}$

设 $\vec{u},$ $\vec{v},$ $\vec{w}$ 是 $t$ 的函数,证明下列乘积法则:$\begin{aligned}(\vec{u}\times\vec{v})'&=\vec{u}'\times\vec{v}+\vec{u}\times\vec{v}' \\ (\vec{u}\bullet\vec{v})'&=\vec{u}'\bullet\vec{v}+\vec{u}\bullet\vec{v}'\end{aligned}$。

答案 Answer:We will show it using the summation convention and permutation symbol $\begin{aligned}((\vec{u}\times\vec{v})')_i&=((\vec{u}\times\vec{v})_i)'=(\epsilon_{ijk}u_jv_k)' \\ &=\epsilon_{ijk}u_j'v_k+\epsilon_{ijk}u_kv_k'=(\vec{u}'\times\vec{v}+\vec{u}\times\vec{v}')_i\end{aligned}$ and so $(\vec{u}\times\vec{v})'=\vec{u}'\times\vec{v}+\vec{u}\times\vec{v}'$.

答案:我们用求和约定与置换符号来证明:$\begin{aligned}((\vec{u}\times\vec{v})')_i&=((\vec{u}\times\vec{v})_i)'=(\epsilon_{ijk}u_jv_k)' \\ &=\epsilon_{ijk}u_j'v_k+\epsilon_{ijk}u_kv_k'=(\vec{u}'\times\vec{v}+\vec{u}\times\vec{v}')_i\end{aligned}$,于是 $(\vec{u}\times\vec{v})'=\vec{u}'\times\vec{v}+\vec{u}\times\vec{v}'$。

习题 51 Exercise 51

Here are some vectors. $\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}2\\7\\-4\end{array}\right],$ $\left[\begin{array}{r}5\\7\\-10\end{array}\right],$ $\left[\begin{array}{r}12\\17\\-24\end{array}\right]$ Describe the span of these vectors as the span of as few vectors as possible.

这里有一些向量:$\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}2\\7\\-4\end{array}\right],$ $\left[\begin{array}{r}5\\7\\-10\end{array}\right],$ $\left[\begin{array}{r}12\\17\\-24\end{array}\right]$。把这些向量的生成子空间表示为尽可能少的向量的生成子空间。

习题 52 Exercise 52

Here are some vectors. $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}12\\29\\-24\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\end{array}\right],$ $\left[\begin{array}{r}2\\9\\-4\end{array}\right],$ $\left[\begin{array}{r}5\\12\\-10\end{array}\right].$ Describe the span of these vectors as the span of as few vectors as possible.

这里有一些向量:$\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}12\\29\\-24\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\end{array}\right],$ $\left[\begin{array}{r}2\\9\\-4\end{array}\right],$ $\left[\begin{array}{r}5\\12\\-10\end{array}\right].$ 把这些向量的生成子空间表示为尽可能少的向量的生成子空间。

习题 53 Exercise 53

Here are some vectors. $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-2\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\0\\2\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-1\end{array}\right]$ Describe the span of these vectors as the span of as few vectors as possible.

这里有一些向量:$\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-2\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\0\\2\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-1\end{array}\right]$。把这些向量的生成子空间表示为尽可能少的向量的生成子空间。

习题 54 Exercise 54

Here are some vectors. $\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-3\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\1\\2\end{array}\right]$ Now here is another vector: $\left[\begin{array}{r}1\\2\\-1\end{array}\right]$ Is this vector in the span of the first four vectors? If it is, exhibit a linear combination of the first four vectors which equals this vector, using as few vectors as possible in the linear combination.

这里有一些向量:$\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-3\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\1\\2\end{array}\right]$。现在还有另一个向量:$\left[\begin{array}{r}1\\2\\-1\end{array}\right]$。这个向量是否在前四个向量的生成子空间中?若是,请给出前四个向量中一个等于该向量的线性组合,并且所用向量尽可能少。

习题 55 Exercise 55

Here are some vectors. $\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-3\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\1\\2\end{array}\right]$ Now here is another vector: $\left[\begin{array}{r}2\\-3\\-4\end{array}\right]$ Is this vector in the span of the first four vectors? If it is, exhibit a linear combination of the first four vectors which equals this vector, using as few vectors as possible in the linear combination.

这里有一些向量:$\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-3\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\1\\2\end{array}\right]$。现在还有另一个向量:$\left[\begin{array}{r}2\\-3\\-4\end{array}\right]$。这个向量是否在前四个向量的生成子空间中?若是,请给出前四个向量中一个等于该向量的线性组合,并且所用向量尽可能少。

习题 56 Exercise 56

Here are some vectors. $\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-3\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-1\end{array}\right]$ Now here is another vector: $\left[\begin{array}{r}1\\9\\1\end{array}\right]$ Is this vector in the span of the first four vectors? If it is, exhibit a linear combination of the first four vectors which equals this vector, using as few vectors as possible in the linear combination.

这里有一些向量:$\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-3\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-1\end{array}\right]$。现在还有另一个向量:$\left[\begin{array}{r}1\\9\\1\end{array}\right]$。这个向量是否在前四个向量的生成子空间中?若是,请给出前四个向量中一个等于该向量的线性组合,并且所用向量尽可能少。

习题 57 Exercise 57

Here are some vectors, $\left[\begin{array}{r}1\\-1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\0\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-5\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\5\\2\end{array}\right]$ Now here is another vector: $\left[\begin{array}{r}1\\1\\-1\end{array}\right]$ Is this vector in the span of the first four vectors? If it is, exhibit a linear combination of the first four vectors which equals this vector, using as few vectors as possible in the linear combination.

这里有一些向量:$\left[\begin{array}{r}1\\-1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\0\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-5\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\5\\2\end{array}\right]$。现在还有另一个向量:$\left[\begin{array}{r}1\\1\\-1\end{array}\right]$。这个向量是否在前四个向量的生成子空间中?若是,请给出前四个向量中一个等于该向量的线性组合,并且所用向量尽可能少。

习题 58 Exercise 58

Here are some vectors. $\left[\begin{array}{r}1\\-1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\0\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-5\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\5\\2\end{array}\right]$ Now here is another vector: $\left[\begin{array}{r}1\\1\\-1\end{array}\right]$ Is this vector in the span of the first four vectors? If it is, exhibit a linear combination of the first four vectors which equals this vector, using as few vectors as possible in the linear combination.

这里有一些向量:$\left[\begin{array}{r}1\\-1\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\0\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\-5\\-2\end{array}\right],$ $\left[\begin{array}{r}-1\\5\\2\end{array}\right]$。现在还有另一个向量:$\left[\begin{array}{r}1\\1\\-1\end{array}\right]$。这个向量是否在前四个向量的生成子空间中?若是,请给出前四个向量中一个等于该向量的线性组合,并且所用向量尽可能少。

习题 59 Exercise 59

Here are some vectors. $\left[\begin{array}{r}1\\0\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}2\\-2\\-3\end{array}\right],$ $\left[\begin{array}{r}-1\\4\\2\end{array}\right]$ Now here is another vector: $\left[\begin{array}{r}-1\\-4\\2\end{array}\right]$ Is this vector in the span of the first four vectors? If it is, exhibit a linear combination of the first four vectors which equals this vector, using as few vectors as possible in the linear combination.

这里有一些向量:$\left[\begin{array}{r}1\\0\\-2\end{array}\right],$ $\left[\begin{array}{r}1\\1\\-2\end{array}\right],$ $\left[\begin{array}{r}2\\-2\\-3\end{array}\right],$ $\left[\begin{array}{r}-1\\4\\2\end{array}\right]$。现在还有另一个向量:$\left[\begin{array}{r}-1\\-4\\2\end{array}\right]$。这个向量是否在前四个向量的生成子空间中?若是,请给出前四个向量中一个等于该向量的线性组合,并且所用向量尽可能少。

习题 60 Exercise 60

Suppose $\{\vec{x}_{1},\cdots ,\vec{x}_{k}\}$ is a set of vectors from $\mathbb{R}^n$. Show that $\vec{0}$ is in $\mathrm{span}\{\vec{x}_{1},\cdots ,\vec{x}_{k}\}$.

设 $\{\vec{x}_{1},\cdots ,\vec{x}_{k}\}$ 是 $\mathbb{R}^n$ 中的一组向量。证明 $\vec{0}$ 在 $\mathrm{span}\{\vec{x}_{1},\cdots ,\vec{x}_{k}\}$ 中。

答案 Answer:$\sum\limits_{i=1}^k 0\vec{x}_{k}=\vec{0}$

答案:$\sum\limits_{i=1}^k 0\vec{x}_{k}=\vec{0}$

习题 61 Exercise 61

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. Also give a linearly independent set of vectors which has the same span as the given vectors. $\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right],$ $\left[\begin{array}{r}1\\10\\2\\1\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。另外给出与这些向量具有相同生成子空间的一组线性无关向量。$\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right],$ $\left[\begin{array}{r}1\\10\\2\\1\end{array}\right]$

习题 62 Exercise 62

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. Also give a linearly independent set of vectors which has the same span as the given vectors. $\left[\begin{array}{r}-1\\-2\\2\\3\end{array}\right],$ $\left[\begin{array}{r}-3\\-4\\3\\3\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\4\\3\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\6\\4\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。另外给出与这些向量具有相同生成子空间的一组线性无关向量。$\left[\begin{array}{r}-1\\-2\\2\\3\end{array}\right],$ $\left[\begin{array}{r}-3\\-4\\3\\3\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\4\\3\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\6\\4\end{array}\right]$

习题 63 Exercise 63

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. Also give a linearly independent set of vectors which has the same span as the given vectors. $\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\6\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}-1\\-4\\1\\-1\end{array}\right],$ $\left[\begin{array}{r}1\\6\\-2\\1\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。另外给出与这些向量具有相同生成子空间的一组线性无关向量。$\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\6\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}-1\\-4\\1\\-1\end{array}\right],$ $\left[\begin{array}{r}1\\6\\-2\\1\end{array}\right]$

习题 64 Exercise 64

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. Also give a linearly independent set of vectors which has the same span as the given vectors. $\left[\begin{array}{r}1\\-1\\3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\6\\34\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\7\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\8\\1\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。另外给出与这些向量具有相同生成子空间的一组线性无关向量。$\left[\begin{array}{r}1\\-1\\3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\6\\34\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\7\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\8\\1\end{array}\right]$

习题 65 Exercise 65

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. $\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}-3\\-10\\3\\-3\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。$\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}-3\\-10\\3\\-3\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right]$

习题 66 Exercise 66

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. Also give a linearly independent set of vectors which has the same span as the given vectors. $\left[\begin{array}{r}1\\3\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-5\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-4\\1\end{array}\right],$ $\left[\begin{array}{r}1\\10\\-14\\1\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。另外给出与这些向量具有相同生成子空间的一组线性无关向量。$\left[\begin{array}{r}1\\3\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-5\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-4\\1\end{array}\right],$ $\left[\begin{array}{r}1\\10\\-14\\1\end{array}\right]$

习题 67 Exercise 67

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. Also give a linearly independent set of vectors which has the same span as the given vectors. $\left[\begin{array}{r}1\\0\\3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\1\\8\\1\end{array}\right],$ $\left[\begin{array}{r}1\\7\\34\\1\end{array}\right],$ $\left[\begin{array}{r}1\\1\\7\\1\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。另外给出与这些向量具有相同生成子空间的一组线性无关向量。$\left[\begin{array}{r}1\\0\\3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\1\\8\\1\end{array}\right],$ $\left[\begin{array}{r}1\\7\\34\\1\end{array}\right],$ $\left[\begin{array}{r}1\\1\\7\\1\end{array}\right]$

习题 68 Exercise 68

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. Also give a linearly independent set of vectors which has the same span as the given vectors. $\left[\begin{array}{r}1\\4\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\7\\-5\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。另外给出与这些向量具有相同生成子空间的一组线性无关向量。$\left[\begin{array}{r}1\\4\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\7\\-5\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right]$

习题 69 Exercise 69

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. $\left[\begin{array}{r}1\\2\\2\\-4\end{array}\right],$ $\left[\begin{array}{r}3\\4\\1\\-4\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\0\\4\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\-2\\5\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。$\left[\begin{array}{r}1\\2\\2\\-4\end{array}\right],$ $\left[\begin{array}{r}3\\4\\1\\-4\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\0\\4\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\-2\\5\end{array}\right]$

习题 70 Exercise 70

Are the following vectors linearly independent? If they are, explain why and if they are not, exhibit one of them as a linear combination of the others. Also give a linearly independent set of vectors which has the same span as the given vectors. $\left[\begin{array}{r}2\\3\\1\\-3\end{array}\right],$ $\left[\begin{array}{r}-5\\-6\\0\\3\end{array}\right],$ $\left[\begin{array}{r}-1\\-2\\1\\3\end{array}\right],$ $\left[\begin{array}{r}-1\\-2\\0\\4\end{array}\right]$

下列向量是否线性无关?若是,请解释原因;若不是,请把其中一个表示成其余向量的线性组合。另外给出与这些向量具有相同生成子空间的一组线性无关向量。$\left[\begin{array}{r}2\\3\\1\\-3\end{array}\right],$ $\left[\begin{array}{r}-5\\-6\\0\\3\end{array}\right],$ $\left[\begin{array}{r}-1\\-2\\1\\3\end{array}\right],$ $\left[\begin{array}{r}-1\\-2\\0\\4\end{array}\right]$

习题 71 Exercise 71

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\1\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\-2\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\2\\0\\1\end{array}\right],$ $\left[\begin{array}{r}1\\-1\\-1\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\1\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\2\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\-2\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\2\\0\\1\end{array}\right],$ $\left[\begin{array}{r}1\\-1\\-1\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 72 Exercise 72

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\2\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}4\\3\\-1\\4\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\2\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}4\\3\\-1\\4\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 73 Exercise 73

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\1\\0\\1\end{array}\right],$ $\left[\begin{array}{r}1\\2\\1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\-2\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-5\\-7\\2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\2\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\1\\0\\1\end{array}\right],$ $\left[\begin{array}{r}1\\2\\1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\-2\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-5\\-7\\2\end{array}\right],$ $\left[\begin{array}{r}1\\2\\2\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 74 Exercise 74

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\2\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\-1\\1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-3\\3\\2\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\2\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\-1\\1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-3\\3\\2\end{array}\right],$ $\left[\begin{array}{r}1\\3\\-2\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 75 Exercise 75

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\4\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}4\\11\\-1\\4\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-3\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\4\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}4\\11\\-1\\4\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-3\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 76 Exercise 76

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}-\frac{3}{2}\\-\frac{9}{2}\\ \frac{3}{2}\\ -\frac{3}{2}\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-1\\-2\\2\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}-\frac{3}{2}\\-\frac{9}{2}\\ \frac{3}{2}\\ -\frac{3}{2}\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-1\\-2\\2\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 77 Exercise 77

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-1\\-2\\2\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-1\\-2\\2\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 78 Exercise 78

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\4\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\1\\1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\1\\3\\2\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\4\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\1\\1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\1\\3\\2\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 79 Exercise 79

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\-1\\3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\7\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\8\\1\end{array}\right],$ $\left[\begin{array}{r}4\\-9\\-6\\4\end{array}\right],$ $\left[\begin{array}{r}1\\0\\8\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\-1\\3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\7\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\8\\1\end{array}\right],$ $\left[\begin{array}{r}4\\-9\\-6\\4\end{array}\right],$ $\left[\begin{array}{r}1\\0\\8\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 80 Exercise 80

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\-1\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}-3\\3\\3\\-3\end{array}\right],$ $\left[\begin{array}{r}1\\0\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-9\\-2\\2\end{array}\right],$ $\left[\begin{array}{r}1\\0\\0\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\-1\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}-3\\3\\3\\-3\end{array}\right],$ $\left[\begin{array}{r}1\\0\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\-9\\-2\\2\end{array}\right],$ $\left[\begin{array}{r}1\\0\\0\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 81 Exercise 81

Here are some vectors in $\mathbb{R}^4$. $\left[\begin{array}{r}1\\b+1\\a\\1\end{array}\right],$ $\left[\begin{array}{r}3\\3b+3\\3a\\3\end{array}\right],$ $\left[\begin{array}{r}1\\b+2\\2a+1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\2b-5\\-5a-7\\2\end{array}\right],$ $\left[\begin{array}{r}1\\b+2\\2a+2\\1\end{array}\right]$ These vectors can't possibly be linearly independent. Tell why. Next obtain a linearly independent subset of these vectors which has the same span as these vectors. In other words, find a basis for the span of these vectors.

这里是一些 $\mathbb{R}^4$ 中的向量:$\left[\begin{array}{r}1\\b+1\\a\\1\end{array}\right],$ $\left[\begin{array}{r}3\\3b+3\\3a\\3\end{array}\right],$ $\left[\begin{array}{r}1\\b+2\\2a+1\\1\end{array}\right],$ $\left[\begin{array}{r}2\\2b-5\\-5a-7\\2\end{array}\right],$ $\left[\begin{array}{r}1\\b+2\\2a+2\\1\end{array}\right]$。这些向量不可能线性无关,请说明原因。接着取这些向量的一个线性无关子集,使其生成子空间与这些向量的相同。换言之,为这些向量的生成子空间找一组基。

习题 82 Exercise 82

Let $H=\mathrm{span}\left\{\left[\begin{array}{r}2\\1\\1\\1\end{array}\right],$ $\left[\begin{array}{r}-1\\0\\-1\\-1\end{array}\right],$ $\left[\begin{array}{r}5\\2\\3\\3\end{array}\right],$ $\left[\begin{array}{r}-1\\1\\-2\\-2\end{array}\right]\right\}.$ Find the dimension of $H$ and determine a basis.

设 $H=\mathrm{span}\left\{\left[\begin{array}{r}2\\1\\1\\1\end{array}\right],$ $\left[\begin{array}{r}-1\\0\\-1\\-1\end{array}\right],$ $\left[\begin{array}{r}5\\2\\3\\3\end{array}\right],$ $\left[\begin{array}{r}-1\\1\\-2\\-2\end{array}\right]\right\}.$ 求 $H$ 的维数并确定一组基。

习题 83 Exercise 83

Let $H$ denote $\mathrm{span}\left\{\left[\begin{array}{r}0\\1\\1\\-1\end{array}\right],$ $\left[\begin{array}{r}-1\\-1\\-2\\2\end{array}\right],$ $\left[\begin{array}{r}2\\3\\5\\-5\end{array}\right],$ $\left[\begin{array}{r}0\\1\\2\\-2\end{array}\right]\right\}.$ Find the dimension of $H$ and determine a basis.

设 $H$ 表示 $\mathrm{span}\left\{\left[\begin{array}{r}0\\1\\1\\-1\end{array}\right],$ $\left[\begin{array}{r}-1\\-1\\-2\\2\end{array}\right],$ $\left[\begin{array}{r}2\\3\\5\\-5\end{array}\right],$ $\left[\begin{array}{r}0\\1\\2\\-2\end{array}\right]\right\}.$ 求 $H$ 的维数并确定一组基。

习题 84 Exercise 84

Let $H$ denote $\mathrm{span}\left\{\left[\begin{array}{r}-2\\1\\1\\-3\end{array}\right],\ \left[\begin{array}{r}-9\\4\\3\\-9\end{array}\right],\ \left[\begin{array}{r}-33\\15\\12\\-36\end{array}\right],\ \left[\begin{array}{r}-22\\10\\8\\-24\end{array}\right]\right\}.$ Find the dimension of $H$ and determine a basis.

设 $H$ 表示 $\mathrm{span}\left\{\left[\begin{array}{r}-2\\1\\1\\-3\end{array}\right],\ \left[\begin{array}{r}-9\\4\\3\\-9\end{array}\right],\ \left[\begin{array}{r}-33\\15\\12\\-36\end{array}\right],\ \left[\begin{array}{r}-22\\10\\8\\-24\end{array}\right]\right\}$。求 $H$ 的维数并确定一组基。

习题 85 Exercise 85

Let $H$ denote $\mathrm{span}\left\{\left[\begin{array}{r}-1\\1\\-1\\-2\end{array}\right],\ \left[\begin{array}{r}-4\\3\\-2\\-4\end{array}\right],\ \left[\begin{array}{r}-3\\2\\-1\\-2\end{array}\right],\ \left[\begin{array}{r}-1\\1\\-2\\-4\end{array}\right],\ \left[\begin{array}{r}-7\\5\\-3\\-6\end{array}\right]\right\}.$ Find the dimension of $H$ and determine a basis.

设 $H$ 表示 $\mathrm{span}\left\{\left[\begin{array}{r}-1\\1\\-1\\-2\end{array}\right],\ \left[\begin{array}{r}-4\\3\\-2\\-4\end{array}\right],\ \left[\begin{array}{r}-3\\2\\-1\\-2\end{array}\right],\ \left[\begin{array}{r}-1\\1\\-2\\-4\end{array}\right],\ \left[\begin{array}{r}-7\\5\\-3\\-6\end{array}\right]\right\}$。求 $H$ 的维数并确定一组基。

习题 86 Exercise 86

Let $H$ denote $\mathrm{span}\left\{\left[\begin{array}{r}2\\3\\2\\1\end{array}\right],\ \left[\begin{array}{r}8\\15\\6\\3\end{array}\right],\ \left[\begin{array}{r}3\\6\\2\\1\end{array}\right],\ \left[\begin{array}{r}4\\6\\6\\3\end{array}\right],\ \left[\begin{array}{r}8\\15\\6\\3\end{array}\right]\right\}.$ Find the dimension of $H$ and determine a basis.

设 $H$ 表示 $\mathrm{span}\left\{\left[\begin{array}{r}2\\3\\2\\1\end{array}\right],\ \left[\begin{array}{r}8\\15\\6\\3\end{array}\right],\ \left[\begin{array}{r}3\\6\\2\\1\end{array}\right],\ \left[\begin{array}{r}4\\6\\6\\3\end{array}\right],\ \left[\begin{array}{r}8\\15\\6\\3\end{array}\right]\right\}$。求 $H$ 的维数并确定一组基。

习题 87 Exercise 87

Let $H$ denote $\mathrm{span}\left\{\left[\begin{array}{r}0\\2\\0\\-1\end{array}\right],\ \left[\begin{array}{r}-1\\6\\0\\-2\end{array}\right],\ \left[\begin{array}{r}-2\\16\\0\\-6\end{array}\right],\ \left[\begin{array}{r}-3\\22\\0\\-8\end{array}\right]\right\}.$ Find the dimension of $H$ and determine a basis.

设 $H$ 表示 $\mathrm{span}\left\{\left[\begin{array}{r}0\\2\\0\\-1\end{array}\right],\ \left[\begin{array}{r}-1\\6\\0\\-2\end{array}\right],\ \left[\begin{array}{r}-2\\16\\0\\-6\end{array}\right],\ \left[\begin{array}{r}-3\\22\\0\\-8\end{array}\right]\right\}$。求 $H$ 的维数并确定一组基。

习题 88 Exercise 88

Let $H$ denote $\mathrm{span}\left\{\left[\begin{array}{r}5\\1\\1\\4\end{array}\right],\ \left[\begin{array}{r}14\\3\\2\\8\end{array}\right],\ \left[\begin{array}{r}38\\8\\6\\24\end{array}\right],\ \left[\begin{array}{r}47\\10\\7\\28\end{array}\right],\ \left[\begin{array}{r}10\\2\\3\\12\end{array}\right]\right\}.$ Find the dimension of $H$ and determine a basis.

设 $H$ 表示 $\mathrm{span}\left\{\left[\begin{array}{r}5\\1\\1\\4\end{array}\right],\ \left[\begin{array}{r}14\\3\\2\\8\end{array}\right],\ \left[\begin{array}{r}38\\8\\6\\24\end{array}\right],\ \left[\begin{array}{r}47\\10\\7\\28\end{array}\right],\ \left[\begin{array}{r}10\\2\\3\\12\end{array}\right]\right\}$。求 $H$ 的维数并确定一组基。

习题 89 Exercise 89

Let $H$ denote $\mathrm{span}\left\{\left[\begin{array}{r}6\\1\\1\\5\end{array}\right],\ \left[\begin{array}{r}17\\3\\2\\10\end{array}\right],\ \left[\begin{array}{r}52\\9\\7\\35\end{array}\right],\ \left[\begin{array}{r}18\\3\\4\\20\end{array}\right]\right\}.$ Find the dimension of $H$ and determine a basis.

设 $H$ 表示 $\mathrm{span}\left\{\left[\begin{array}{r}6\\1\\1\\5\end{array}\right],\ \left[\begin{array}{r}17\\3\\2\\10\end{array}\right],\ \left[\begin{array}{r}52\\9\\7\\35\end{array}\right],\ \left[\begin{array}{r}18\\3\\4\\20\end{array}\right]\right\}$。求 $H$ 的维数并确定一组基。

习题 90 Exercise 90

Let $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:\sin(u_{1})=1\right\}.$ Is $M$ a subspace? Explain.

设 $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:\sin(u_{1})=1\right\}$。$M$ 是子空间吗?请说明。

答案 Answer:No. Let $\vec{u}=\left[\begin{array}{c}\frac{\pi}{2}\\0\\0\\0\end{array}\right]$. Then $2\vec{u}\notin M$ although $\vec{u}\in M$.

答案:不是。设 $\vec{u}=\left[\begin{array}{c}\frac{\pi}{2}\\0\\0\\0\end{array}\right]$,则 $2\vec{u}\notin M$,尽管 $\vec{u}\in M$。

习题 91 Exercise 91

Let $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:\left|u_{1}\right|\leq 4\right\}.$ Is $M$ a subspace? Explain.

设 $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:\left|u_{1}\right|\leq 4\right\}$。$M$ 是子空间吗?请说明。

答案 Answer:No. $\left[\begin{array}{c}1\\0\\0\\0\end{array}\right]\in M$ but $10\left[\begin{array}{c}1\\0\\0\\0\end{array}\right]\notin M$.

答案:不是。$\left[\begin{array}{c}1\\0\\0\\0\end{array}\right]\in M$,但 $10\left[\begin{array}{c}1\\0\\0\\0\end{array}\right]\notin M$。

习题 92 Exercise 92

Let $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:u_{i}\geq 0\text{ for each }i=1,2,3,4 \right\}.$ Is $M$ a subspace? Explain.

设 $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:u_{i}\geq 0, i=1,2,3,4 \right\}$。$M$ 是子空间吗?请说明。

答案 Answer:This is not a subspace. $\left[\begin{array}{c}1\\1\\1\\1\end{array}\right]$ is in it. However, $(-1)\left[\begin{array}{c}1\\1\\1\\1\end{array}\right]$ is not.

答案:这不是子空间。$\left[\begin{array}{c}1\\1\\1\\1\end{array}\right]$ 在其中,然而 $(-1)\left[\begin{array}{c}1\\1\\1\\1\end{array}\right]$ 不在其中。

习题 93 Exercise 93

Let $\vec{w}$, $\vec{w}_{1}$ be given vectors in $\mathbb{R}^{4}$ and define $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:\vec{w}\bullet\vec{u}=0\text{ and }\vec{w}_{1}\bullet\vec{u}=0\right\}.$ Is $M$ a subspace? Explain.

设 $\vec{w}$、$\vec{w}_{1}$ 是 $\mathbb{R}^{4}$ 中给定的向量,定义 $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:\vec{w}\cdot\vec{u}=0\text{ 且 }\vec{w}_{1}\cdot\vec{u}=0\right\}$。$M$ 是子空间吗?请说明。

答案 Answer:This is a subspace because it is closed with respect to vector addition and scalar multiplication.

答案:这是子空间,因为它在向量加法与标量乘法下封闭。

习题 94 Exercise 94

Let $\vec{w}\in\mathbb{R}^{4}$ and let $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:\vec{w}\bullet\vec{u}=0\right\}.$ Is $M$ a subspace? Explain.

设 $\vec{w}\in\mathbb{R}^{4}$,令 $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:\vec{w}\cdot\vec{u}=0\right\}$。$M$ 是子空间吗?请说明。

答案 Answer:Yes, this is a subspace because it is closed with respect to vector addition and scalar multiplication.

答案:是的,这是子空间,因为它在向量加法与标量乘法下封闭。

习题 95 Exercise 95

Let $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:u_{3}\geq u_{1}\right\}.$ Is $M$ a subspace? Explain.

设 $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:u_{3}\geq u_{1}\right\}$。$M$ 是子空间吗?请说明。

答案 Answer:This is not a subspace. $\left[\begin{array}{c}0\\0\\1\\0\end{array}\right]$ is in it. However $(-1)\left[\begin{array}{c}0\\0\\1\\0\end{array}\right]=\left[\begin{array}{r}0\\0\\-1\\0\end{array}\right]$ is not.

答案:这不是子空间。$\left[\begin{array}{c}0\\0\\1\\0\end{array}\right]$ 在其中,然而 $(-1)\left[\begin{array}{c}0\\0\\1\\0\end{array}\right]=\left[\begin{array}{r}0\\0\\-1\\0\end{array}\right]$ 不在其中。

习题 96 Exercise 96

Let $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:u_{3}=u_{1}=0\right\}.$ Is $M$ a subspace? Explain.

设 $M=\left\{\vec{u}=\left[\begin{array}{c}u_{1}\\u_{2}\\u_{3}\\u_{4}\end{array}\right]\in\mathbb{R}^{4}:u_{3}=u_{1}=0\right\}$。$M$ 是子空间吗?请说明。

答案 Answer:This is a subspace. It is closed with respect to vector addition and scalar multiplication.

答案:这是子空间。它在向量加法与标量乘法下封闭。

习题 97 Exercise 97

Consider the set of vectors $S$ given by $S=\left\{\left[\begin{array}{c}4u+v-5w\\12u+6v-6w\\4u+4v+4w\end{array}\right]:u,v,w\in\mathbb{R}\right\}.$ Is $S$ a subspace of $\mathbb{R}^{3}$? If so, explain why, give a basis for the subspace and find its dimension.

考虑由 $S=\left\{\left[\begin{array}{c}4u+v-5w\\12u+6v-6w\\4u+4v+4w\end{array}\right]:u,v,w\in\mathbb{R}\right\}$ 给出的向量组 $S$。$S$ 是 $\mathbb{R}^{3}$ 的子空间吗?如果是,请解释原因,给出该子空间的一组基并求其维数。

习题 98 Exercise 98

Consider the set of vectors $S$ given by $S=\left\{\left[\begin{array}{c}2u+6v+7w\\-3u-9v-12w\\2u+6v+6w\\u+3v+3w\end{array}\right]:u,v,w\in\mathbb{R}\right\}.$ Is $S$ a subspace of $\mathbb{R}^{4}$? If so, explain why, give a basis for the subspace and find its dimension.

考虑由 $S=\left\{\left[\begin{array}{c}2u+6v+7w\\-3u-9v-12w\\2u+6v+6w\\u+3v+3w\end{array}\right]:u,v,w\in\mathbb{R}\right\}$ 给出的向量组 $S$。$S$ 是 $\mathbb{R}^{4}$ 的子空间吗?如果是,请解释原因,给出该子空间的一组基并求其维数。

习题 99 Exercise 99

Consider the set of vectors $S$ given by $S=\left\{\left[\begin{array}{c}2u+v\\6v-3u+3w\\3v-6u+3w\end{array}\right]:u,v,w\in\mathbb{R}\right\}.$ Is this set of vectors a subspace of $\mathbb{R}^{3}$? If so, explain why, give a basis for the subspace and find its dimension.

考虑由 $S=\left\{\left[\begin{array}{c}2u+v\\6v-3u+3w\\3v-6u+3w\end{array}\right]:u,v,w\in\mathbb{R}\right\}$ 给出的向量组 $S$。这组向量是 $\mathbb{R}^{3}$ 的子空间吗?如果是,请解释原因,给出该子空间的一组基并求其维数。

习题 100 Exercise 100

Consider the vectors of the form $\left\{\left[\begin{array}{c}2u+v+7w\\u-2v+w\\-6v-6w\end{array}\right]:u,v,w\in\mathbb{R}\right\}.$ Is this set of vectors a subspace of $\mathbb{R}^{3}$? If so, explain why, give a basis for the subspace and find its dimension.

考虑形如 $\left\{\left[\begin{array}{c}2u+v+7w\\u-2v+w\\-6v-6w\end{array}\right]:u,v,w\in\mathbb{R}\right\}$ 的向量。这组向量是 $\mathbb{R}^{3}$ 的子空间吗?如果是,请解释原因,给出该子空间的一组基并求其维数。

习题 101 Exercise 101

Consider the vectors of the form $\left\{\left[\begin{array}{c}3u+v+11w\\18u+6v+66w\\28u+8v+100w\end{array}\right]:u,v,w\in\mathbb{R}\right\}.$ Is this set of vectors a subspace of $\mathbb{R}^{3}$? If so, explain why, give a basis for the subspace and find its dimension.

考虑形如 $\left\{\left[\begin{array}{c}3u+v+11w\\18u+6v+66w\\28u+8v+100w\end{array}\right]:u,v,w\in\mathbb{R}\right\}$ 的向量。这组向量是 $\mathbb{R}^{3}$ 的子空间吗?如果是,请解释原因,给出该子空间的一组基并求其维数。

习题 102 Exercise 102

Consider the vectors of the form $\left\{\left[\begin{array}{c}3u+v\\2w-4u\\2w-2v-8u\end{array}\right]:u,v,w\in\mathbb{R}\right\}.$ Is this set of vectors a subspace of $\mathbb{R}^{3}$? If so, explain why, give a basis for the subspace and find its dimension.

考虑形如 $\left\{\left[\begin{array}{c}3u+v\\2w-4u\\2w-2v-8u\end{array}\right]:u,v,w\in\mathbb{R}\right\}$ 的向量。这组向量是 $\mathbb{R}^{3}$ 的子空间吗?如果是,请解释原因,给出该子空间的一组基并求其维数。

习题 103 Exercise 103

Consider the set of vectors $S$ given by $\left\{\left[\begin{array}{c}u+v+w\\2u+2v+4w\\u+v+w\\0\end{array}\right]:u,v,w\in\mathbb{R}\right\}.$ Is $S$ a subspace of $\mathbb{R}^{4}$? If so, explain why, give a basis for the subspace and find its dimension.

考虑由 $\left\{\left[\begin{array}{c}u+v+w\\2u+2v+4w\\u+v+w\\0\end{array}\right]:u,v,w\in\mathbb{R}\right\}$ 给出的向量组 $S$。$S$ 是 $\mathbb{R}^{4}$ 的子空间吗?如果是,请解释原因,给出该子空间的一组基并求其维数。

习题 104 Exercise 104

Consider the set of vectors $S$ given by $\left\{\left[\begin{array}{c}v\\-3u-3w\\8u-4v+4w\end{array}\right]:u,v,w\in\mathbb{R}\right\}.$ Is $S$ a subspace of $\mathbb{R}^{4}$? If so, explain why, give a basis for the subspace and find its dimension.

考虑由 $\left\{\left[\begin{array}{c}v\\-3u-3w\\8u-4v+4w\end{array}\right]:u,v,w\in\mathbb{R}\right\}$ 给出的向量组 $S$。$S$ 是 $\mathbb{R}^{4}$ 的子空间吗?如果是,请解释原因,给出该子空间的一组基并求其维数。

习题 105 Exercise 105

If you have $5$ vectors in $\mathbb{R}^{5}$ and the vectors are linearly independent, can it always be concluded they span $\mathbb{R}^{5}$? Explain.

如果你在 $\mathbb{R}^{5}$ 中有 $5$ 个向量且它们线性无关,能否总得出它们张成 $\mathbb{R}^{5}$ 的结论?请解释。

答案 Answer:Yes. If not, there would exist a vector not in the span. But then you could add in this vector and obtain a linearly independent set of vectors with more vectors than a basis.

答案:可以。若不然,就会存在一个不在其张成中的向量。但那样就可以把该向量加进去,得到一个比基所含向量更多的线性无关向量组。

习题 106 Exercise 106

If you have $6$ vectors in $\mathbb{R}^{5}$, is it possible they are linearly independent? Explain.

如果你在 $\mathbb{R}^{5}$ 中有 $6$ 个向量,它们是否可能线性无关?请解释。

答案 Answer:They can't be.

答案:不可能。

习题 107 Exercise 107

Suppose $A$ is an $m\times n$ matrix and $\left\{\vec{w}_{1},\cdots,\vec{w}_{k}\right\}$ is a linearly independent set of vectors in $A(\mathbb{R}^{n})\subseteq\mathbb{R}^{m}$. Now suppose $A\vec{z}_{i}=\vec{w}_{i}$. Show $\left\{\vec{z}_{1},\cdots,\vec{z}_{k}\right\}$ is also independent.

设 $A$ 是 $m\times n$ 矩阵,且 $\left\{\vec{w}_{1},\cdots,\vec{w}_{k}\right\}$ 是 $A(\mathbb{R}^{n})\subseteq\mathbb{R}^{m}$ 中的线性无关向量组。现设 $A\vec{z}_{i}=\vec{w}_{i}$。证明 $\left\{\vec{z}_{1},\cdots,\vec{z}_{k}\right\}$ 也线性无关。

答案 Answer:Say $\sum\limits_{i=1}^{k}c_{i}\vec{z}_{i}=\vec{0}$. Then apply $A$ to it as follows. $\sum\limits_{i=1}^{k}c_{i}A\vec{z}_{i}=\sum\limits_{i=1}^{k}c_{i}\vec{w}_{i}=\vec{0}$ and so, by linear independence of the $\vec{w}_{i}$, it follows that each $c_{i}=0$.

答案:设 $\sum\limits_{i=1}^{k}c_{i}\vec{z}_{i}=\vec{0}$。然后对两边应用 $A$ 如下:$\sum\limits_{i=1}^{k}c_{i}A\vec{z}_{i}=\sum\limits_{i=1}^{k}c_{i}\vec{w}_{i}=\vec{0}$,于是由 $\vec{w}_{i}$ 的线性无关性,得每个 $c_{i}=0$。

习题 108 Exercise 108

Suppose $V$, $W$ are subspaces of $\mathbb{R}^{n}$. Let $V\cap W$ be all vectors which are in both $V$ and $W$. Show that $V\cap W$ is a subspace also.

设 $V$、$W$ 是 $\mathbb{R}^{n}$ 的子空间。令 $V\cap W$ 表示既在 $V$ 中又在 $W$ 中的所有向量。证明 $V\cap W$ 也是子空间。

答案 Answer:If $\vec{x},\vec{y}\in V\cap W$, then for scalars $\alpha,\beta$, the linear combination $\alpha\vec{x}+\beta\vec{y}$ must be in both $V$ and $W$ since they are both subspaces.

答案:若 $\vec{x},\vec{y}\in V\cap W$,则对任意标量 $\alpha,\beta$,线性组合 $\alpha\vec{x}+\beta\vec{y}$ 必同时在 $V$ 和 $W$ 中,因为二者都是子空间。

习题 109 Exercise 109

Suppose $V$ and $W$ both have dimension equal to $7$ and they are subspaces of $\mathbb{R}^{10}$. What are the possibilities for the dimension of $V\cap W$? Hint: Remember that a linear independent set can be extended to form a basis.

设 $V$ 和 $W$ 的维数都等于 $7$,而且它们都是 $\mathbb{R}^{10}$ 的子空间。$V\cap W$ 的维数可能有哪些值?提示:记住线性无关组可以扩充成一组基。

习题 110 Exercise 110

Suppose $V$ has dimension $p$ and $W$ has dimension $q$ and they are each contained in a subspace, $U$ which has dimension equal to $n$ where $n>\max(p,q)$. What are the possibilities for the dimension of $V\cap W$? Hint: Remember that a linearly independent set can be extended to form a basis.

设 $V$ 的维数为 $p$、$W$ 的维数为 $q$,它们都包含于一个维数等于 $n$ 的子空间 $U$ 中,其中 $n>\max(p,q)$。$V\cap W$ 的维数可能有哪些值?提示:记住线性无关组可以扩充成一组基。

答案 Answer:Let $\left\{\vec{x}_{1},\cdots,\vec{x}_{k}\right\}$ be a basis for $V\cap W$. Then there is a basis for $V$ and $W$ which are respectively $\left\{\vec{x}_{1},\cdots,\vec{x}_{k},\vec{y}_{k+1},\cdots,\vec{y}_{p}\right\}$, $\left\{\vec{x}_{1},\cdots,\vec{x}_{k},\vec{z}_{k+1},\cdots,\vec{z}_{q}\right\}$. It follows that you must have $k+p-k+q-k\leq n$ and so you must have $p+q-n\leq k$.

答案:令 $\left\{\vec{x}_{1},\cdots,\vec{x}_{k}\right\}$ 是 $V\cap W$ 的一组基。则分别存在 $V$ 与 $W$ 的基 $\left\{\vec{x}_{1},\cdots,\vec{x}_{k},\vec{y}_{k+1},\cdots,\vec{y}_{p}\right\}$、$\left\{\vec{x}_{1},\cdots,\vec{x}_{k},\vec{z}_{k+1},\cdots,\vec{z}_{q}\right\}$。由此可知必须有 $k+p-k+q-k\leq n$,从而必须有 $p+q-n\leq k$。

习题 111 Exercise 111

Suppose $A$ is an $m\times n$ matrix and $B$ is an $n\times p$ matrix. Show that $\text{dim}(\text{ker}(AB))\leq\text{dim}(\text{ker}(A))+\text{dim}(\text{ker}(B))$. Consider the subspace, $B(\mathbb{R}^{p})\cap\text{ker}(A)$ and suppose a basis for this subspace is $\left\{\vec{w}_{1},\cdots,\vec{w}_{k}\right\}$. Now suppose $\left\{\vec{u}_{1},\cdots,\vec{u}_{r}\right\}$ is a basis for $\text{ker}(B)$. Let $\left\{\vec{z}_{1},\cdots,\vec{z}_{k}\right\}$ be such that $B\vec{z}_{i}=\vec{w}_{i}$ and argue that $\text{ker}(AB)\subseteq\text{span}\left\{\vec{u}_{1},\cdots,\vec{u}_{r},\vec{z}_{1},\cdots,\vec{z}_{k}\right\}$.

设 $A$ 是 $m\times n$ 矩阵、$B$ 是 $n\times p$ 矩阵。证明 $\text{dim}(\text{ker}(AB))\leq\text{dim}(\text{ker}(A))+\text{dim}(\text{ker}(B))$。考虑子空间 $B(\mathbb{R}^{p})\cap\text{ker}(A)$,设该子空间的一组基为 $\left\{\vec{w}_{1},\cdots,\vec{w}_{k}\right\}$。再设 $\left\{\vec{u}_{1},\cdots,\vec{u}_{r}\right\}$ 是 $\text{ker}(B)$ 的一组基。令 $\left\{\vec{z}_{1},\cdots,\vec{z}_{k}\right\}$ 满足 $B\vec{z}_{i}=\vec{w}_{i}$,并论证 $\text{ker}(AB)\subseteq\text{span}\left\{\vec{u}_{1},\cdots,\vec{u}_{r},\vec{z}_{1},\cdots,\vec{z}_{k}\right\}$。

答案 Answer:Here is how you do this. Suppose $AB\vec{x}=\vec{0}$. Then $B\vec{x}\in\text{ker}(A)\cap B(\mathbb{R}^{p})$ and so $B\vec{x}=\sum\limits_{i=1}^{k}B\vec{z}_{i}$ showing that $\vec{x}-\sum\limits_{i=1}^{k}\vec{z}_{i}\in\text{ker}(B)$. Consider $B(\mathbb{R}^{p})\cap\text{ker}(A)$ and let a basis be $\left\{\vec{w}_{1},\cdots,\vec{w}_{k}\right\}$. Then each $\vec{w}_{i}$ is of the form $B\vec{z}_{i}=\vec{w}_{i}$. Therefore, $\left\{\vec{z}_{1},\cdots,\vec{z}_{k}\right\}$ is linearly independent and $AB\vec{z}_{i}=0$. Now let $\left\{\vec{u}_{1},\cdots,\vec{u}_{r}\right\}$ be a basis for $\text{ker}(B)$. If $AB\vec{x}=\vec{0}$, then $B\vec{x}\in\text{ker}(A)\cap B(\mathbb{R}^{p})$ and so $B\vec{x}=\sum\limits_{i=1}^{k}c_{i}B\vec{z}_{i}$ which implies $\vec{x}-\sum\limits_{i=1}^{k}c_{i}\vec{z}_{i}\in\text{ker}(B)$ and so it is of the form $\vec{x}-\sum\limits_{i=1}^{k}c_{i}\vec{z}_{i}=\sum\limits_{j=1}^{r}d_{j}\vec{u}_{j}$. It follows that if $AB\vec{x}=\vec{0}$ so that $\vec{x}\in\text{ker}(AB)$, then $\vec{x}\in\text{span}\left(\vec{z}_{1},\cdots,\vec{z}_{k},\vec{u}_{1},\cdots,\vec{u}_{r}\right)$. Therefore, $\begin{aligned}\text{dim}(\text{ker}(AB))&\leq k+r=\text{dim}(B(\mathbb{R}^{p})\cap\text{ker}(A))+\text{dim}(\text{ker}(B))\\ &\leq\text{dim}(\text{ker}(A))+\text{dim}(\text{ker}(B))\end{aligned}$

答案:做法如下。设 $AB\vec{x}=\vec{0}$,则 $B\vec{x}\in\text{ker}(A)\cap B(\mathbb{R}^{p})$,于是 $B\vec{x}=\sum\limits_{i=1}^{k}B\vec{z}_{i}$,说明 $\vec{x}-\sum\limits_{i=1}^{k}\vec{z}_{i}\in\text{ker}(B)$。考虑 $B(\mathbb{R}^{p})\cap\text{ker}(A)$,设其基为 $\left\{\vec{w}_{1},\cdots,\vec{w}_{k}\right\}$,则每个 $\vec{w}_{i}$ 形如 $B\vec{z}_{i}=\vec{w}_{i}$。因此 $\left\{\vec{z}_{1},\cdots,\vec{z}_{k}\right\}$ 线性无关且 $AB\vec{z}_{i}=0$。再令 $\left\{\vec{u}_{1},\cdots,\vec{u}_{r}\right\}$ 是 $\text{ker}(B)$ 的一组基。若 $AB\vec{x}=\vec{0}$,则 $B\vec{x}\in\text{ker}(A)\cap B(\mathbb{R}^{p})$,于是 $B\vec{x}=\sum\limits_{i=1}^{k}c_{i}B\vec{z}_{i}$,意味着 $\vec{x}-\sum\limits_{i=1}^{k}c_{i}\vec{z}_{i}\in\text{ker}(B)$,从而它形如 $\vec{x}-\sum\limits_{i=1}^{k}c_{i}\vec{z}_{i}=\sum\limits_{j=1}^{r}d_{j}\vec{u}_{j}$。于是若 $AB\vec{x}=\vec{0}$(即 $\vec{x}\in\text{ker}(AB)$),则 $\vec{x}\in\text{span}\left(\vec{z}_{1},\cdots,\vec{z}_{k},\vec{u}_{1},\cdots,\vec{u}_{r}\right)$。因此,$\begin{aligned}\text{dim}(\text{ker}(AB))&\leq k+r=\text{dim}(B(\mathbb{R}^{p})\cap\text{ker}(A))+\text{dim}(\text{ker}(B))\\ &\leq\text{dim}(\text{ker}(A))+\text{dim}(\text{ker}(B))\end{aligned}$

习题 112 Exercise 112

Show that if $A$ is an $m\times n$ matrix, then $\text{ker}(A)$ is a subspace of $\mathbb{R}^{n}$.

证明:若 $A$ 是 $m\times n$ 矩阵,则 $\text{ker}(A)$ 是 $\mathbb{R}^{n}$ 的子空间。

答案 Answer:If $\vec{x}$, $\vec{y}\in\text{ker}(A)$ then $A(a\vec{x}+b\vec{y})=aA\vec{x}+bA\vec{y}=a\vec{0}+b\vec{0}=\vec{0}$ and so $\text{ker}(A)$ is closed under linear combinations. Hence it is a subspace.

答案:若 $\vec{x}$、$\vec{y}\in\text{ker}(A)$,则 $A(a\vec{x}+b\vec{y})=aA\vec{x}+bA\vec{y}=a\vec{0}+b\vec{0}=\vec{0}$,故 $\text{ker}(A)$ 在任意线性组合下封闭,因此它是子空间。

习题 113 Exercise 113

Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrrrr}1&3&0&-2&0&3\\3&9&1&-7&0&8\\1&3&1&-3&1&-1\\1&3&-1&-1&-2&10\end{array}\right]$

求下列矩阵的秩,并分别求出行空间与列空间的一组基。$\left[\begin{array}{rrrrrr}1&3&0&-2&0&3\\3&9&1&-7&0&8\\1&3&1&-3&1&-1\\1&3&-1&-1&-2&10\end{array}\right]$

习题 114 Exercise 114

Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrrrr}1&3&0&-2&7&3\\3&9&1&-7&23&8\\1&3&1&-3&9&2\\1&3&-1&-1&5&4\end{array}\right]$

求下列矩阵的秩,并分别求出行空间与列空间的一组基。$\left[\begin{array}{rrrrrr}1&3&0&-2&7&3\\3&9&1&-7&23&8\\1&3&1&-3&9&2\\1&3&-1&-1&5&4\end{array}\right]$

习题 115 Exercise 115

Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrrrr}1&0&3&0&7&0\\3&1&10&0&23&0\\1&1&4&1&7&0\\1&-1&2&-2&9&1\end{array}\right]$

求下列矩阵的秩,并分别求出行空间与列空间的一组基。$\left[\begin{array}{rrrrrr}1&0&3&0&7&0\\3&1&10&0&23&0\\1&1&4&1&7&0\\1&-1&2&-2&9&1\end{array}\right]$

习题 116 Exercise 116

Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrr}1&0&3\\3&1&10\\1&1&4\\1&-1&2\end{array}\right]$

求下列矩阵的秩,并分别求出行空间与列空间的一组基。$\left[\begin{array}{rrr}1&0&3\\3&1&10\\1&1&4\\1&-1&2\end{array}\right]$

习题 117 Exercise 117

Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrrr}0&0&-1&0&1\\1&2&3&-2&-18\\1&2&2&-1&-11\\-1&-2&-2&1&11\end{array}\right]$

求下列矩阵的秩,并分别求出行空间与列空间的一组基。$\left[\begin{array}{rrrrr}0&0&-1&0&1\\1&2&3&-2&-18\\1&2&2&-1&-11\\-1&-2&-2&1&11\end{array}\right]$

习题 118 Exercise 118

Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrr}1&0&3&0\\3&1&10&0\\-1&1&-2&1\\1&-1&2&-2\end{array}\right]$

求下列矩阵的秩,并分别求出行空间与列空间的一组基。$\left[\begin{array}{rrrr}1&0&3&0\\3&1&10&0\\-1&1&-2&1\\1&-1&2&-2\end{array}\right]$

习题 119 Exercise 119

Find $\text{ker}(A)$ for the following matrices.

求下列矩阵的 $\text{ker}(A)$。

1. $A=\left[\begin{array}{rr}2&3\\4&6\end{array}\right]$

1. $A=\left[\begin{array}{rr}2&3\\4&6\end{array}\right]$

2. $A=\left[\begin{array}{rrr}1&0&-1\\-1&1&3\\3&2&1\end{array}\right]$

2. $A=\left[\begin{array}{rrr}1&0&-1\\-1&1&3\\3&2&1\end{array}\right]$

3. $A=\left[\begin{array}{rrr}2&4&0\\3&6&-2\\1&2&-2\end{array}\right]$

3. $A=\left[\begin{array}{rrr}2&4&0\\3&6&-2\\1&2&-2\end{array}\right]$

4. $A=\left[\begin{array}{rrrr}2&-1&3&5\\2&0&1&2\\6&4&-5&-6\\0&2&-4&-6\end{array}\right]$

4. $A=\left[\begin{array}{rrrr}2&-1&3&5\\2&0&1&2\\6&4&-5&-6\\0&2&-4&-6\end{array}\right]$

习题 120 Exercise 120

Determine whether the following set of vectors is orthogonal. If it is orthogonal, determine whether it is also orthonormal. $\left[\begin{array}{c}\frac{1}{6}\sqrt{2}\sqrt{3}\\\frac{1}{3}\sqrt{2}\sqrt{3}\\-\frac{1}{6}\sqrt{2}\sqrt{3}\end{array}\right],\ \left[\begin{array}{c}\frac{1}{2}\sqrt{2}\\0\\\frac{1}{2}\sqrt{2}\end{array}\right],\ \left[\begin{array}{c}-\frac{1}{3}\sqrt{3}\\\frac{1}{3}\sqrt{3}\\\frac{1}{3}\sqrt{3}\end{array}\right]$ If the set of vectors is orthogonal but not orthonormal, give an orthonormal set of vectors which has the same span.

判断下列向量组是否正交。若正交,再判断它是否同时也标准正交。$\left[\begin{array}{c}\frac{1}{6}\sqrt{2}\sqrt{3}\\\frac{1}{3}\sqrt{2}\sqrt{3}\\-\frac{1}{6}\sqrt{2}\sqrt{3}\end{array}\right],\ \left[\begin{array}{c}\frac{1}{2}\sqrt{2}\\0\\\frac{1}{2}\sqrt{2}\end{array}\right],\ \left[\begin{array}{c}-\frac{1}{3}\sqrt{3}\\\frac{1}{3}\sqrt{3}\\\frac{1}{3}\sqrt{3}\end{array}\right]$ 若该向量组正交但不标准正交,给出一个具有相同张成空间的标准正交向量组。

习题 121 Exercise 121

Determine whether the following set of vectors is orthogonal. If it is orthogonal, determine whether it is also orthonormal. $\left[\begin{array}{r}1\\2\\-1\end{array}\right],\ \left[\begin{array}{r}1\\0\\1\end{array}\right],\ \left[\begin{array}{r}-1\\1\\1\end{array}\right]$ If the set of vectors is orthogonal but not orthonormal, give an orthonormal set of vectors which has the same span.

判断下列向量组是否正交。若正交,再判断它是否同时也标准正交。$\left[\begin{array}{r}1\\2\\-1\end{array}\right],\ \left[\begin{array}{r}1\\0\\1\end{array}\right],\ \left[\begin{array}{r}-1\\1\\1\end{array}\right]$ 若该向量组正交但不标准正交,给出一个具有相同张成空间的标准正交向量组。

习题 122 Exercise 122

Determine whether the following set of vectors is orthogonal. If it is orthogonal, determine whether it is also orthonormal. $\left[\begin{array}{r}1\\-1\\1\end{array}\right],\ \left[\begin{array}{r}2\\1\\-1\end{array}\right],\ \left[\begin{array}{r}0\\1\\1\end{array}\right]$ If the set of vectors is orthogonal but not orthonormal, give an orthonormal set of vectors which has the same span.

判断下列向量组是否正交。若正交,再判断它是否同时也标准正交。$\left[\begin{array}{r}1\\-1\\1\end{array}\right],\ \left[\begin{array}{r}2\\1\\-1\end{array}\right],\ \left[\begin{array}{r}0\\1\\1\end{array}\right]$ 若该向量组正交但不标准正交,给出一个具有相同张成空间的标准正交向量组。

习题 123 Exercise 123

Determine whether the following set of vectors is orthogonal. If it is orthogonal, determine whether it is also orthonormal. $\left[\begin{array}{r}1\\-1\\1\end{array}\right],\ \left[\begin{array}{r}2\\1\\-1\end{array}\right],\ \left[\begin{array}{r}1\\2\\1\end{array}\right]$ If the set of vectors is orthogonal but not orthonormal, give an orthonormal set of vectors which has the same span.

判断下列向量组是否正交。若正交,再判断它是否同时也标准正交。$\left[\begin{array}{r}1\\-1\\1\end{array}\right],\ \left[\begin{array}{r}2\\1\\-1\end{array}\right],\ \left[\begin{array}{r}1\\2\\1\end{array}\right]$ 若该向量组正交但不标准正交,给出一个具有相同张成空间的标准正交向量组。

习题 124 Exercise 124

Determine whether the following set of vectors is orthogonal. If it is orthogonal, determine whether it is also orthonormal. $\left[\begin{array}{r}1\\0\\0\\0\end{array}\right],\ \left[\begin{array}{r}0\\1\\-1\\0\end{array}\right],\ \left[\begin{array}{r}0\\0\\0\\1\end{array}\right]$ If the set of vectors is orthogonal but not orthonormal, give an orthonormal set of vectors which has the same span.

判断下列向量组是否正交。若正交,再判断它是否同时也标准正交。$\left[\begin{array}{r}1\\0\\0\\0\end{array}\right],\ \left[\begin{array}{r}0\\1\\-1\\0\end{array}\right],\ \left[\begin{array}{r}0\\0\\0\\1\end{array}\right]$ 若该向量组正交但不标准正交,给出一个具有相同张成空间的标准正交向量组。

习题 125 Exercise 125

Here are some matrices. Label according to whether they are symmetric, skew symmetric, or orthogonal.

下面是几个矩阵。请按照对称矩阵、反对称矩阵或正交矩阵来标注它们。

1. $\left[\begin{array}{ccc}1&0&0\\0&\frac{1}{\sqrt{2}}&-\frac{1}{\sqrt{2}}\\0&\frac{1}{\sqrt{2}}&\frac{1}{\sqrt{2}}\end{array}\right]$

1. $\left[\begin{array}{ccc}1&0&0\\0&\frac{1}{\sqrt{2}}&-\frac{1}{\sqrt{2}}\\0&\frac{1}{\sqrt{2}}&\frac{1}{\sqrt{2}}\end{array}\right]$

2. $\left[\begin{array}{ccc}1&2&-3\\2&1&4\\-3&4&7\end{array}\right]$

2. $\left[\begin{array}{ccc}1&2&-3\\2&1&4\\-3&4&7\end{array}\right]$

3. $\left[\begin{array}{ccc}0&-2&-3\\2&0&-4\\3&4&0\end{array}\right]$

3. $\left[\begin{array}{ccc}0&-2&-3\\2&0&-4\\3&4&0\end{array}\right]$

答案 Answer:1. Orthogonal; 2. Symmetric; 3. Skew Symmetric

答案:1. 正交;2. 对称;3. 反对称

习题 126 Exercise 126

For $U$ an orthogonal matrix, explain why $\left\|U\vec{x}\right\|=\left\|\vec{x}\right\|$ for any vector $\vec{x}$. Next explain why if $U$ is an $n\times n$ matrix with the property that $\left\|U\vec{x}\right\|=\left\|\vec{x}\right\|$ for all vectors, $\vec{x}$, then $U$ must be orthogonal. Thus the orthogonal matrices are exactly those which preserve length.

对于正交矩阵 $U$,解释为什么对任意向量 $\vec{x}$ 都有 $\left\|U\vec{x}\right\|=\left\|\vec{x}\right\|$。接着解释:若 $U$ 是 $n\times n$ 矩阵,且对所有向量 $\vec{x}$ 具有性质 $\left\|U\vec{x}\right\|=\left\|\vec{x}\right\|$,那么 $U$ 必为正交矩阵。因此正交矩阵正是那些保长度的矩阵。

答案 Answer:$\left\|U\vec{x}\right\|^{2}=U\vec{x}\bullet U\vec{x}=U^{T}U\vec{x}\bullet\vec{x}=I\vec{x}\bullet\vec{x}=\left\|\vec{x}\right\|^{2}$. Next suppose distance is preserved by $U$. Then $\begin{aligned} (U(\vec{x}+\vec{y}))\bullet (U(\vec{x}+\vec{y}))&=\left\|Ux\right\|^{2}+\left\|Uy\right\|^{2}+2(Ux\bullet Uy)\\ &=\left\|\vec{x}\right\|^{2}+\left\|\vec{y}\right\|^{2}+2(U^{T}U\vec{x}\bullet\vec{y})\end{aligned}$ But since $U$ preserves distances, it is also the case that $U(\vec{x}+\vec{y})\bullet U(\vec{x}+\vec{y}))=\left\|\vec{x}\right\|^{2}+\left\|\vec{y}\right\|^{2}+2(\vec{x}\bullet\vec{y})$. Hence $\vec{x}\bullet\vec{y}=U^{T}U\vec{x}\bullet\vec{y}$ and so $(U^{T}U-I)\vec{x})\bullet\vec{y}=0$. Since $y$ is arbitrary, it follows that $U^{T}U-I=0$. Thus $U$ is orthogonal.

答案:$\left\|U\vec{x}\right\|^{2}=U\vec{x}\bullet U\vec{x}=U^{T}U\vec{x}\bullet\vec{x}=I\vec{x}\bullet\vec{x}=\left\|\vec{x}\right\|^{2}$。其次假设 $U$ 保持距离。则 $\begin{aligned} (U(\vec{x}+\vec{y}))\bullet (U(\vec{x}+\vec{y}))&=\left\|Ux\right\|^{2}+\left\|Uy\right\|^{2}+2(Ux\bullet Uy)\\ &=\left\|\vec{x}\right\|^{2}+\left\|\vec{y}\right\|^{2}+2(U^{T}U\vec{x}\bullet\vec{y})\end{aligned}$ 但由于 $U$ 保持距离,同样有 $U(\vec{x}+\vec{y})\bullet U(\vec{x}+\vec{y}))=\left\|\vec{x}\right\|^{2}+\left\|\vec{y}\right\|^{2}+2(\vec{x}\bullet\vec{y})$。因此 $\vec{x}\bullet\vec{y}=U^{T}U\vec{x}\bullet\vec{y}$,从而 $(U^{T}U-I)\vec{x})\bullet\vec{y}=0$。由于 $y$ 任意,可得 $U^{T}U-I=0$。于是 $U$ 是正交矩阵。

习题 127 Exercise 127

Suppose $U$ is an orthogonal $n\times n$ matrix. Explain why $\text{rank}(U)=n$.

设 $U$ 是 $n\times n$ 正交矩阵。解释为什么 $\text{rank}(U)=n$。

答案 Answer:You could observe that $\det(UU^{T})=(\det(U))^{2}-1$ so $\det(U)\neq 0$.

答案:可以观察到 $\det(UU^{T})=(\det(U))^{2}-1$,所以 $\det(U)\neq 0$。

习题 128 Exercise 128

Fill in the missing entries to make the matrix orthogonal. $\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\underline{\hspace{1.2em}}&\underline{\hspace{1.2em}}\\\underline{\hspace{1.2em}}&\frac{\sqrt{6}}{3}&\underline{\hspace{1.2em}}\end{array}\right].$

填入缺失的元素使矩阵成为正交矩阵。$\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\underline{\hspace{1.2em}}&\underline{\hspace{1.2em}}\\\underline{\hspace{1.2em}}&\frac{\sqrt{6}}{3}&\underline{\hspace{1.2em}}\end{array}\right].$

答案 Answer:$$\begin{aligned} &\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&a\\0&\frac{\sqrt{6}}{3}&b\end{array}\right]\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&a\\0&\frac{\sqrt{6}}{3}&b\end{array}\right]^{T}\\ =&\left[\begin{array}{ccc}1&\frac{1}{3}\sqrt{3}a-\frac{1}{3}&\frac{1}{3}\sqrt{3}b-\frac{1}{3}\\ \frac{1}{3}\sqrt{3}a-\frac{1}{3}&a^{2}+\frac{2}{3}&ab-\frac{1}{3}\\ \frac{1}{3}\sqrt{3}b-\frac{1}{3}&ab-\frac{1}{3}&b^{2}+\frac{2}{3}\end{array}\right]\end{aligned}$$ This requires $$a=1/\sqrt{3},\quad b=1/\sqrt{3}.$$ $$\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&1/\sqrt{3}\\0&\frac{\sqrt{6}}{3}&1/\sqrt{3}\end{array}\right]\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&1/\sqrt{3}\\0&\frac{\sqrt{6}}{3}&1/\sqrt{3}\end{array}\right]^{T}=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&1\end{array}\right]$$

答案:$$\begin{aligned} &\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&a\\0&\frac{\sqrt{6}}{3}&b\end{array}\right]\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&a\\0&\frac{\sqrt{6}}{3}&b\end{array}\right]^{T}\\ =&\left[\begin{array}{ccc}1&\frac{1}{3}\sqrt{3}a-\frac{1}{3}&\frac{1}{3}\sqrt{3}b-\frac{1}{3}\\ \frac{1}{3}\sqrt{3}a-\frac{1}{3}&a^{2}+\frac{2}{3}&ab-\frac{1}{3}\\ \frac{1}{3}\sqrt{3}b-\frac{1}{3}&ab-\frac{1}{3}&b^{2}+\frac{2}{3}\end{array}\right]\end{aligned}$$ 这要求 $$a=1/\sqrt{3},\quad b=1/\sqrt{3}.$$ $$\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&1/\sqrt{3}\\0&\frac{\sqrt{6}}{3}&1/\sqrt{3}\end{array}\right]\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&1/\sqrt{3}\\0&\frac{\sqrt{6}}{3}&1/\sqrt{3}\end{array}\right]^{T}=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&1\end{array}\right]$$

习题 129 Exercise 129

Fill in the missing entries to make the matrix orthogonal. $\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\underline{\hspace{1.2em}}&\underline{\hspace{1.2em}}\\\underline{\hspace{1.2em}}&0&\underline{\hspace{1.2em}}\end{array}\right]$

填入缺失的元素使矩阵成为正交矩阵。$\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\underline{\hspace{1.2em}}&\underline{\hspace{1.2em}}\\\underline{\hspace{1.2em}}&0&\underline{\hspace{1.2em}}\end{array}\right]$

答案 Answer:$$\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&a\\-\frac{1}{3}&0&b\end{array}\right]\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&a\\-\frac{1}{3}&0&b\end{array}\right]^{T}=\left[\begin{array}{ccc}1&\frac{1}{6}\sqrt{2}a-\frac{1}{18}&\frac{1}{6}\sqrt{2}b-\frac{2}{9}\\\frac{1}{6}\sqrt{2}a-\frac{1}{18}&a^{2}+\frac{17}{18}&ab-\frac{2}{9}\\\frac{1}{6}\sqrt{2}b-\frac{2}{9}&ab-\frac{2}{9}&b^{2}+\frac{1}{9}\end{array}\right]$$ This requires $$a=\frac{1}{3\sqrt{2}},\quad b=\frac{4}{3\sqrt{2}}.$$ $$\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&\frac{1}{3\sqrt{2}}\\-\frac{1}{3}&0&\frac{4}{3\sqrt{2}}\end{array}\right]\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&\frac{1}{3\sqrt{2}}\\-\frac{1}{3}&0&\frac{4}{3\sqrt{2}}\end{array}\right]^{T}=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&1\end{array}\right]$$

答案:$$\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&a\\-\frac{1}{3}&0&b\end{array}\right]\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&a\\-\frac{1}{3}&0&b\end{array}\right]^{T}=\left[\begin{array}{ccc}1&\frac{1}{6}\sqrt{2}a-\frac{1}{18}&\frac{1}{6}\sqrt{2}b-\frac{2}{9}\\\frac{1}{6}\sqrt{2}a-\frac{1}{18}&a^{2}+\frac{17}{18}&ab-\frac{2}{9}\\\frac{1}{6}\sqrt{2}b-\frac{2}{9}&ab-\frac{2}{9}&b^{2}+\frac{1}{9}\end{array}\right]$$ 这要求 $$a=\frac{1}{3\sqrt{2}},\quad b=\frac{4}{3\sqrt{2}}.$$ $$\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&\frac{1}{3\sqrt{2}}\\-\frac{1}{3}&0&\frac{4}{3\sqrt{2}}\end{array}\right]\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&\frac{1}{3\sqrt{2}}\\-\frac{1}{3}&0&\frac{4}{3\sqrt{2}}\end{array}\right]^{T}=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&1\end{array}\right]$$

习题 130 Exercise 130

Fill in the missing entries to make the matrix orthogonal. $\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&\underline{\hspace{1.2em}}\\\frac{2}{3}&0&\underline{\hspace{1.2em}}\\\underline{\hspace{1.2em}}&\underline{\hspace{1.2em}}&\frac{4}{15}\sqrt{5}\end{array}\right]$

填入缺失的元素使矩阵成为正交矩阵。$\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&\underline{\hspace{1.2em}}\\\frac{2}{3}&0&\underline{\hspace{1.2em}}\\\underline{\hspace{1.2em}}&\underline{\hspace{1.2em}}&\frac{4}{15}\sqrt{5}\end{array}\right]$

答案 Answer:Try $$\begin{aligned}&\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&c\\\frac{2}{3}&0&d\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&c\\\frac{2}{3}&0&d\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]^{T}\\ =&\left[\begin{array}{ccc}c^{2}+\frac{41}{45}&cd+\frac{2}{9}&\frac{4}{15}\sqrt{5}c-\frac{8}{45}\\cd+\frac{2}{9}&d^{2}+\frac{4}{9}&\frac{4}{15}\sqrt{5}d+\frac{4}{9}\\\frac{4}{15}\sqrt{5}c-\frac{8}{45}&\frac{4}{15}\sqrt{5}d+\frac{4}{9}&1\end{array}\right]\end{aligned}$$ This requires that $c=\frac{2}{3\sqrt{5}}$, $d=\frac{-5}{3\sqrt{5}}$. $$\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&\frac{2}{3\sqrt{5}}\\\frac{2}{3}&0&\frac{-5}{3\sqrt{5}}\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&\frac{2}{3\sqrt{5}}\\\frac{2}{3}&0&\frac{-5}{3\sqrt{5}}\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]^{T}=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&1\end{array}\right]$$

答案:尝试 $$\begin{aligned}&\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&c\\\frac{2}{3}&0&d\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&c\\\frac{2}{3}&0&d\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]^{T}\\ =&\left[\begin{array}{ccc}c^{2}+\frac{41}{45}&cd+\frac{2}{9}&\frac{4}{15}\sqrt{5}c-\frac{8}{45}\\cd+\frac{2}{9}&d^{2}+\frac{4}{9}&\frac{4}{15}\sqrt{5}d+\frac{4}{9}\\\frac{4}{15}\sqrt{5}c-\frac{8}{45}&\frac{4}{15}\sqrt{5}d+\frac{4}{9}&1\end{array}\right]\end{aligned}$$ 这要求 $c=\frac{2}{3\sqrt{5}}$、$d=\frac{-5}{3\sqrt{5}}$。$$\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&\frac{2}{3\sqrt{5}}\\\frac{2}{3}&0&\frac{-5}{3\sqrt{5}}\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&\frac{2}{3\sqrt{5}}\\\frac{2}{3}&0&\frac{-5}{3\sqrt{5}}\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]^{T}=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&1\end{array}\right]$$

习题 131 Exercise 131

Find an orthonormal basis for the span of each of the following sets of vectors.

求下列各组向量张成空间的一组标准正交基。

1. $\left[\begin{array}{r}3\\-4\\0\end{array}\right],\ \left[\begin{array}{r}7\\-1\\0\end{array}\right],\ \left[\begin{array}{r}1\\7\\1\end{array}\right]$

1. $\left[\begin{array}{r}3\\-4\\0\end{array}\right],\ \left[\begin{array}{r}7\\-1\\0\end{array}\right],\ \left[\begin{array}{r}1\\7\\1\end{array}\right]$

2. $\left[\begin{array}{r}3\\0\\-4\end{array}\right],\ \left[\begin{array}{r}11\\0\\2\end{array}\right],\ \left[\begin{array}{r}1\\1\\7\end{array}\right]$

2. $\left[\begin{array}{r}3\\0\\-4\end{array}\right],\ \left[\begin{array}{r}11\\0\\2\end{array}\right],\ \left[\begin{array}{r}1\\1\\7\end{array}\right]$

3. $\left[\begin{array}{r}3\\0\\-4\end{array}\right],\ \left[\begin{array}{r}5\\0\\10\end{array}\right],\ \left[\begin{array}{r}-7\\1\\1\end{array}\right]$

3. $\left[\begin{array}{r}3\\0\\-4\end{array}\right],\ \left[\begin{array}{r}5\\0\\10\end{array}\right],\ \left[\begin{array}{r}-7\\1\\1\end{array}\right]$

答案 Answer:1. $\left[\begin{array}{c}\frac{3}{5}\\-\frac{4}{5}\\0\end{array}\right],\ \left[\begin{array}{c}\frac{4}{5}\\\frac{3}{5}\\0\end{array}\right],\ \left[\begin{array}{c}0\\0\\1\end{array}\right]$. 2. $\left[\begin{array}{c}\frac{3}{5}\\0\\-\frac{4}{5}\end{array}\right],\ \left[\begin{array}{c}\frac{4}{5}\\0\\\frac{3}{5}\end{array}\right],\ \left[\begin{array}{c}0\\1\\0\end{array}\right]$. 3. $\left[\begin{array}{c}\frac{3}{5}\\0\\-\frac{4}{5}\end{array}\right],\ \left[\begin{array}{c}\frac{4}{5}\\0\\\frac{3}{5}\end{array}\right],\ \left[\begin{array}{c}0\\1\\0\end{array}\right]$

答案:1. $\left[\begin{array}{c}\frac{3}{5}\\-\frac{4}{5}\\0\end{array}\right],\ \left[\begin{array}{c}\frac{4}{5}\\\frac{3}{5}\\0\end{array}\right],\ \left[\begin{array}{c}0\\0\\1\end{array}\right]$。2. $\left[\begin{array}{c}\frac{3}{5}\\0\\-\frac{4}{5}\end{array}\right],\ \left[\begin{array}{c}\frac{4}{5}\\0\\\frac{3}{5}\end{array}\right],\ \left[\begin{array}{c}0\\1\\0\end{array}\right]$。3. $\left[\begin{array}{c}\frac{3}{5}\\0\\-\frac{4}{5}\end{array}\right],\ \left[\begin{array}{c}\frac{4}{5}\\0\\\frac{3}{5}\end{array}\right],\ \left[\begin{array}{c}0\\1\\0\end{array}\right]$

习题 132 Exercise 132

Using the Gram Schmidt process find an orthonormal basis for the following span: $\mathrm{span}\left\{\left[\begin{array}{r}1\\2\\1\end{array}\right],\ \left[\begin{array}{r}2\\-1\\3\end{array}\right],\ \left[\begin{array}{r}1\\0\\0\end{array}\right]\right\}$

用 Gram-Schmidt 过程求下列张成空间的一组标准正交基:$\mathrm{span}\left\{\left[\begin{array}{r}1\\2\\1\end{array}\right],\ \left[\begin{array}{r}2\\-1\\3\end{array}\right],\ \left[\begin{array}{r}1\\0\\0\end{array}\right]\right\}$

答案 Answer:A solution is $\left[\begin{array}{c}\frac{1}{6}\sqrt{6}\\\frac{1}{3}\sqrt{6}\\\frac{1}{6}\sqrt{6}\end{array}\right],\ \left[\begin{array}{c}\frac{3}{10}\sqrt{2}\\-\frac{2}{5}\sqrt{2}\\\frac{1}{2}\sqrt{2}\end{array}\right],\ \left[\begin{array}{c}\frac{7}{15}\sqrt{3}\\-\frac{1}{15}\sqrt{3}\\-\frac{1}{3}\sqrt{3}\end{array}\right]$

答案:一个解是 $\left[\begin{array}{c}\frac{1}{6}\sqrt{6}\\\frac{1}{3}\sqrt{6}\\\frac{1}{6}\sqrt{6}\end{array}\right],\ \left[\begin{array}{c}\frac{3}{10}\sqrt{2}\\-\frac{2}{5}\sqrt{2}\\\frac{1}{2}\sqrt{2}\end{array}\right],\ \left[\begin{array}{c}\frac{7}{15}\sqrt{3}\\-\frac{1}{15}\sqrt{3}\\-\frac{1}{3}\sqrt{3}\end{array}\right]$

习题 133 Exercise 133

Using the Gram Schmidt process find an orthonormal basis for the following span: $\mathrm{span}\left\{\left[\begin{array}{r}1\\2\\1\\0\end{array}\right],\ \left[\begin{array}{r}2\\-1\\3\\1\end{array}\right],\ \left[\begin{array}{r}1\\0\\0\\1\end{array}\right]\right\}$

用 Gram-Schmidt 过程求下列张成空间的一组标准正交基:$\mathrm{span}\left\{\left[\begin{array}{r}1\\2\\1\\0\end{array}\right],\ \left[\begin{array}{r}2\\-1\\3\\1\end{array}\right],\ \left[\begin{array}{r}1\\0\\0\\1\end{array}\right]\right\}$

答案 Answer:Then a solution is $\left[\begin{array}{c}\frac{1}{6}\sqrt{6}\\\frac{1}{3}\sqrt{6}\\\frac{1}{6}\sqrt{6}\\0\end{array}\right],\ \left[\begin{array}{c}\frac{1}{6}\sqrt{2}\sqrt{3}\\-\frac{2}{9}\sqrt{2}\sqrt{3}\\\frac{5}{18}\sqrt{2}\sqrt{3}\\\frac{1}{9}\sqrt{2}\sqrt{3}\end{array}\right],\ \left[\begin{array}{c}\frac{5}{111}\sqrt{3}\sqrt{37}\\\frac{1}{133}\sqrt{3}\sqrt{37}\\-\frac{17}{333}\sqrt{3}\sqrt{37}\\\frac{22}{333}\sqrt{3}\sqrt{37}\end{array}\right]$

答案:一个解是 $\left[\begin{array}{c}\frac{1}{6}\sqrt{6}\\\frac{1}{3}\sqrt{6}\\\frac{1}{6}\sqrt{6}\\0\end{array}\right],\ \left[\begin{array}{c}\frac{1}{6}\sqrt{2}\sqrt{3}\\-\frac{2}{9}\sqrt{2}\sqrt{3}\\\frac{5}{18}\sqrt{2}\sqrt{3}\\\frac{1}{9}\sqrt{2}\sqrt{3}\end{array}\right],\ \left[\begin{array}{c}\frac{5}{111}\sqrt{3}\sqrt{37}\\\frac{1}{133}\sqrt{3}\sqrt{37}\\-\frac{17}{333}\sqrt{3}\sqrt{37}\\\frac{22}{333}\sqrt{3}\sqrt{37}\end{array}\right]$

习题 134 Exercise 134

The set $V=\left\{\left[\begin{array}{c}x\\y\\z\end{array}\right]:2x+3y-z=0\right\}$ is a subspace of $\mathbb{R}^{3}$. Find an orthonormal basis for this subspace.

集合 $V=\left\{\left[\begin{array}{c}x\\y\\z\end{array}\right]:2x+3y-z=0\right\}$ 是 $\mathbb{R}^{3}$ 的一个子空间。求这个子空间的一组标准正交基。

答案 Answer:The subspace is of the form $\left[\begin{array}{c}x\\y\\2x+3y\end{array}\right]$ and a basis is $\left[\begin{array}{c}1\\0\\2\end{array}\right],\ \left[\begin{array}{c}0\\1\\3\end{array}\right]$. Therefore, an orthonormal basis is $\left[\begin{array}{c}\frac{1}{5}\sqrt{5}\\0\\\frac{2}{5}\sqrt{5}\end{array}\right],\ \left[\begin{array}{c}-\frac{3}{35}\sqrt{5}\sqrt{14}\\\frac{1}{14}\sqrt{5}\sqrt{14}\\\frac{3}{70}\sqrt{5}\sqrt{14}\end{array}\right]$

答案:子空间形如 $\left[\begin{array}{c}x\\y\\2x+3y\end{array}\right]$,且一组基为 $\left[\begin{array}{c}1\\0\\2\end{array}\right],\ \left[\begin{array}{c}0\\1\\3\end{array}\right]$。因此,一组标准正交基是 $\left[\begin{array}{c}\frac{1}{5}\sqrt{5}\\0\\\frac{2}{5}\sqrt{5}\end{array}\right],\ \left[\begin{array}{c}-\frac{3}{35}\sqrt{5}\sqrt{14}\\\frac{1}{14}\sqrt{5}\sqrt{14}\\\frac{3}{70}\sqrt{5}\sqrt{14}\end{array}\right]$

习题 135 Exercise 135

Consider the following scalar equation of a plane. $2x-3y+z=0$ Find the orthogonal complement of the vector $\vec{v}=\left[\begin{array}{c}3\\4\\1\end{array}\right]$. Also find the point on the plane which is closest to $(3,4,1)$.

考虑下列平面的数量方程 $2x-3y+z=0$。求向量 $\vec{v}=\left[\begin{array}{c}3\\4\\1\end{array}\right]$ 的正交补。并求平面上离 $(3,4,1)$ 最近的点。

习题 136 Exercise 136

Consider the following scalar equation of a plane. $x+3y+z=0$ Find the orthogonal complement of the vector $\vec{v}=\left[\begin{array}{c}1\\2\\1\end{array}\right]$. Also find the point on the plane which is closest to $(3,4,1)$.

考虑下列平面的数量方程 $x+3y+z=0$。求向量 $\vec{v}=\left[\begin{array}{c}1\\2\\1\end{array}\right]$ 的正交补。并求平面上离 $(3,4,1)$ 最近的点。

习题 137 Exercise 137

Let $\vec{v}$ be a vector and let $\vec{n}$ be a normal vector for a plane through the origin. Find the equation of the line through the point determined by $\vec{v}$ which has direction vector $\vec{n}$. Show that it intersects the plane at the point determined by $\vec{v}-\text{proj}_{\vec{n}}\vec{v}$. Hint: The line:$\vec{v}+t\vec{n}$. It is in the plane if $\vec{n}\bullet(\vec{v}+t\vec{n})=0$. Determine $t$. Then substitute in to the equation of the line.

设 $\vec{v}$ 是一个向量,$\vec{n}$ 是过原点的平面的法向量。求过由 $\vec{v}$ 确定的点、方向向量为 $\vec{n}$ 的直线方程。证明它与平面交于由 $\vec{v}-\text{proj}_{\vec{n}}\vec{v}$ 确定的点。提示:直线:$\vec{v}+t\vec{n}$。若 $\vec{n}\bullet(\vec{v}+t\vec{n})=0$ 则它就在平面内。确定 $t$,然后代入直线方程。

习题 138 Exercise 138

As shown in the above problem, one can find the closest point to $\vec{v}$ in a plane through the origin by finding the intersection of the line through $\vec{v}$ having direction vector equal to the normal vector to the plane with the plane. If the plane does not pass through the origin, this will still work to find the point on the plane closest to the point determined by $\vec{v}$. Here is a relation which defines a plane $2x+y+z=11$ and here is a point: $(1,1,2)$. Find the point on the plane which is closest to this point. Then determine the distance from the point to the plane by taking the distance between these two points. Hint: Line: $(x,y,z)=(1,1,2)+t(2,1,1)$. Now require that it intersect the plane.

如上一题所示,要求过原点的平面内离 $\vec{v}$ 最近的点,可以先求过 $\vec{v}$、方向向量等于平面法向量的直线与平面的交点。若平面不过原点,此法仍可用于求平面上离由 $\vec{v}$ 确定的点最近的点。这里有定义平面的关系式 $2x+y+z=11$ 以及一点 $(1,1,2)$。求该平面上离此点最近的点,然后取这两点之间的距离来确定点到平面的距离。提示:直线:$(x,y,z)=(1,1,2)+t(2,1,1)$。现在要求它与平面相交。

习题 139 Exercise 139

In general, you have a point $(x_{0},y_{0},z_{0})$ and a scalar equation for a plane $ax+by+cz=d$ where $a^{2}+b^{2}+c^{2}>0$. Determine a formula for the closest point on the plane to the given point. Then use this point to get a formula for the distance from the given point to the plane. Hint: Find the line perpendicular to the plane which goes through the given point: $(x,y,z)=(x_{0},y_{0},z_{0})+t(a,b,c)$. Now require that this point satisfy the equation for the plane to determine $t$.

一般地,给定一点 $(x_{0},y_{0},z_{0})$ 和平面数量方程 $ax+by+cz=d$(其中 $a^{2}+b^{2}+c^{2}>0$)。求平面上离给定点最近点的公式,再用该点得到从给定点到平面的距离公式。提示:求过给定点且垂直于平面的直线:$(x,y,z)=(x_{0},y_{0},z_{0})+t(a,b,c)$。现在要求该点满足平面方程以确定 $t$。

习题 140 Exercise 140

Find the least squares solution to the following system. $\begin{aligned}x+2y&=1\\2x+3y&=2\\3x+5y&=4\end{aligned}$

求下列方程组的最小二乘解。$\begin{aligned}x+2y&=1\\2x+3y&=2\\3x+5y&=4\end{aligned}$

答案 Answer:$$\begin{aligned}\left[\begin{array}{cc}1&2\\2&3\\3&5\end{array}\right]^{T}\left[\begin{array}{cc}1&2\\2&3\\3&5\end{array}\right]&=\left[\begin{array}{cc}14&23\\23&38\end{array}\right]\left[\begin{array}{cc}14&23\\23&38\end{array}\right]\left[\begin{array}{c}x\\y\end{array}\right]\\ &=\left[\begin{array}{cc}1&2\\2&3\\3&5\end{array}\right]^{T}\left[\begin{array}{c}1\\2\\4\end{array}\right]=\left[\begin{array}{c}17\\28\end{array}\right]\end{aligned}$$ $$\begin{aligned}\left[\begin{array}{cc}14&23\\23&38\end{array}\right]\left[\begin{array}{c}x\\y\end{array}\right]&=\left[\begin{array}{c}17\\28\end{array}\right]\\ \left[\begin{array}{cc}14&23\\23&38\end{array}\right]\left[\begin{array}{c}x\\y\end{array}\right]&=\left[\begin{array}{c}17\\28\end{array}\right],\end{aligned}$$ Solution is: $$\left[\begin{array}{c}\frac{2}{3}\\\frac{1}{3}\end{array}\right]$$

答案:$$\begin{aligned}\left[\begin{array}{cc}1&2\\2&3\\3&5\end{array}\right]^{T}\left[\begin{array}{cc}1&2\\2&3\\3&5\end{array}\right]&=\left[\begin{array}{cc}14&23\\23&38\end{array}\right]\left[\begin{array}{cc}14&23\\23&38\end{array}\right]\left[\begin{array}{c}x\\y\end{array}\right]\\ &=\left[\begin{array}{cc}1&2\\2&3\\3&5\end{array}\right]^{T}\left[\begin{array}{c}1\\2\\4\end{array}\right]=\left[\begin{array}{c}17\\28\end{array}\right]\end{aligned}$$ $$\begin{aligned}\left[\begin{array}{cc}14&23\\23&38\end{array}\right]\left[\begin{array}{c}x\\y\end{array}\right]&=\left[\begin{array}{c}17\\28\end{array}\right]\\ \left[\begin{array}{cc}14&23\\23&38\end{array}\right]\left[\begin{array}{c}x\\y\end{array}\right]&=\left[\begin{array}{c}17\\28\end{array}\right],\end{aligned}$$ 解为:$$\left[\begin{array}{c}\frac{2}{3}\\\frac{1}{3}\end{array}\right]$$

习题 141 Exercise 141

You are doing experiments and have obtained the ordered pairs, $(0,1),(1,2),(2,3.5),(3,4)$ Find $m$ and $b$ such that $\vec{y}=m\vec{x}+b$ approximates these four points as well as possible.

你做实验得到了有序对 $(0,1),(1,2),(2,3.5),(3,4)$。求 $m$ 和 $b$,使得 $\vec{y}=m\vec{x}+b$ 尽可能好地逼近这四个点。

习题 142 Exercise 142

Suppose you have several ordered triples, $(x_{i},y_{i},z_{i})$. Describe how to find a polynomial such as $z=a+bx+cy+dxy+ex^{2}+fy^{2}$ giving the best fit to the given ordered triples.

假设你有若干有序三元组 $(x_{i},y_{i},z_{i})$。描述如何求一个多项式如 $z=a+bx+cy+dxy+ex^{2}+fy^{2}$,使与给定的有序三元组拟合得最好。

习题 143 Exercise 143

The wind blows from the South at $20$ kilometers per hour and an airplane which flies at $600$ kilometers per hour in still air is heading East. Find the velocity of the airplane and its location after two hours.

风从南方以每小时 $20$ 公里吹来,且一架在静空中以每小时 $600$ 公里飞行的飞机正朝东飞行。求飞机的速度以及两小时后的位置。

习题 144 Exercise 144

The wind blows from the West at $30$ kilometers per hour and an airplane which flies at $400$ kilometers per hour in still air is heading North East. Find the velocity of the airplane and its position after two hours.

风从西方以每小时 $30$ 公里吹来,且一架在静空中以每小时 $400$ 公里飞行的飞机正朝东北方向飞行。求飞机的速度以及两小时后的位置。

习题 145 Exercise 145

The wind blows from the North at $10$ kilometers per hour. An airplane which flies at $300$ kilometers per hour in still air is supposed to go to the point whose coordinates are at $\left(100,100\right).$ In what direction should the airplane fly?

风从北方以每小时 $10$ 公里吹来。一架在静空中以每小时 $300$ 公里飞行的飞机应飞往坐标为 $\left(100,100\right)$ 的点。飞机应向哪个方向飞?

习题 146 Exercise 146

Three forces act on an object. Two are $\left[\begin{array}{r}3\\-1\\-1\end{array}\right]$ and $\left[\begin{array}{r}1\\-3\\4\end{array}\right]$ Newtons. Find the third force if the object is not to move.

三个力作用在一个物体上。其中两个力是 $\left[\begin{array}{r}3\\-1\\-1\end{array}\right]$ 牛和 $\left[\begin{array}{r}1\\-3\\4\end{array}\right]$ 牛。若物体要保持不动,求第三个力。

习题 147 Exercise 147

Three forces act on an object. Two are $\left[\begin{array}{r}6\\-3\\3\end{array}\right]$ and $\left[\begin{array}{r}2\\1\\3\end{array}\right]$ Newtons. Find the third force if the total force on the object is to be $\left[\begin{array}{r}7\\1\\3\end{array}\right].$

三个力作用在一个物体上。其中两个力是 $\left[\begin{array}{r}6\\-3\\3\end{array}\right]$ 牛和 $\left[\begin{array}{r}2\\1\\3\end{array}\right]$ 牛。若物体所受合力要成为 $\left[\begin{array}{r}7\\1\\3\end{array}\right]$,求第三个力。

习题 148 Exercise 148

A river flows West at the rate of $b$ miles per hour. A boat can move at the rate of $8$ miles per hour. Find the smallest value of $b$ such that it is not possible for the boat to proceed directly across the river.

一条河以每小时 $b$ 英里的速度向西流。一艘船能以每小时 $8$ 英里的速度行驶。求使得船无法直接横渡这条河时 $b$ 的最小值。

习题 149 Exercise 149

The wind blows from West to East at a speed of $50$ miles per hour and an airplane which travels at $400$ miles per hour in still air is heading North West. What is the velocity of the airplane relative to the ground? What is the component of this velocity in the direction North?

风以每小时 $50$ 英里的速度从西向东吹,一架在静空中以每小时 $400$ 英里飞行的飞机正朝西北方向飞行。飞机相对于地面的速度是多少?这个速度在正北方向的分量是多少?

答案 Answer:The velocity is the sum of two vectors. $50\vec{i}+\frac{300}{\sqrt{2}}\left(\vec{i}+\vec{j}\right)=\left(50+\frac{300}{\sqrt{2}}\right)\vec{i}+\frac{300}{\sqrt{2}}\vec{j}.$ The component in the direction of North is then $\frac{300}{\sqrt{2}}=150\sqrt{2}$ and the velocity relative to the ground is $\left(50+\frac{300}{\sqrt{2}}\right)\vec{i}+\frac{300}{\sqrt{2}}\vec{j}$

答案:速度是两个向量之和。$50\vec{i}+\frac{300}{\sqrt{2}}\left(\vec{i}+\vec{j}\right)=\left(50+\frac{300}{\sqrt{2}}\right)\vec{i}+\frac{300}{\sqrt{2}}\vec{j}.$ 于是正北方向的分量为 $\frac{300}{\sqrt{2}}=150\sqrt{2}$,而相对地面的速度为 $\left(50+\frac{300}{\sqrt{2}}\right)\vec{i}+\frac{300}{\sqrt{2}}\vec{j}$

习题 150 Exercise 150

The wind blows from West to East at a speed of $60$ miles per hour and an airplane can travel travels at $100$ miles per hour in still air. How many degrees West of North should the airplane head in order to travel exactly North?

风以每小时 $60$ 英里的速度从西向东吹,一架飞机在静空中能以每小时 $100$ 英里飞行。为了恰好朝正北飞行,飞机应朝北偏西多少度飞行?

习题 151 Exercise 151

The wind blows from West to East at a speed of $50$ miles per hour and an airplane which travels at $400$ miles per hour in still air heading somewhat West of North so that, with the wind, it is flying due North. It uses $30.0$ gallons of gas every hour. If it has to travel $600.0$ miles due North, how much gas will it use in flying to its destination?

风以每小时 $50$ 英里的速度从西向东吹,一架在静空中以每小时 $400$ 英里飞行的飞机朝北偏西某方向飞行,使得在风中它正好向正北飞行。它每小时耗油 $30.0$ 加仑。若它必须向正北飞行 $600.0$ 英里,飞抵目的地将耗油多少?

习题 152 Exercise 152

An airplane is flying due north at $150.0$ miles per hour but it is not actually going due North because there is a wind which is pushing the airplane due east at $40.0$ miles per hour. After one hour, the plane starts flying $30^{\circ}$ East of North. Assuming the plane starts at $\left(0,0\right),$ where is it after $2$ hours? Let North be the direction of the positive $y$ axis and let East be the direction of the positive $x$ axis.

一架飞机以每小时 $150.0$ 英里的速度向正北飞行,但它并没有真的向正北运动,因为有一股风以每小时 $40.0$ 英里把它推向正东。一小时后,飞机开始沿北偏东 $30^{\circ}$ 方向飞行。假设飞机从 $\left(0,0\right)$ 出发,$2$ 小时后它在何处?设北为正 $y$ 轴方向、东为正 $x$ 轴方向。

答案 Answer:Velocity of plane for the first hour: $\left[\begin{array}{cc}0&150\end{array}\right]+\left[\begin{array}{cc}40&0\end{array}\right]=\left[\begin{array}{cc}40&150\end{array}\right].$ After one hour it is at $\left(40,150\right).$ Next the velocity of the plane is $150\left[\begin{array}{cc}\frac{1}{2}&\frac{\sqrt{3}}{2}\end{array}\right]+\left[\begin{array}{cc}40&0\end{array}\right]$ in miles per hour. After two hours it is then at $\left(40,150\right)+150\left[\begin{array}{cc}\frac{1}{2}&\frac{\sqrt{3}}{2}\end{array}\right]+\left[\begin{array}{cc}40&0\end{array}\right]=\left[\begin{array}{cc}155&75\sqrt{3}+150\end{array}\right]=\left[\begin{array}{cc}155.0&279.9\end{array}\right]$

答案:第一小时飞机的速度:$\left[\begin{array}{cc}0&150\end{array}\right]+\left[\begin{array}{cc}40&0\end{array}\right]=\left[\begin{array}{cc}40&150\end{array}\right].$ 一小时后它在 $\left(40,150\right).$ 接下来飞机的速度是 $150\left[\begin{array}{cc}\frac{1}{2}&\frac{\sqrt{3}}{2}\end{array}\right]+\left[\begin{array}{cc}40&0\end{array}\right]$(单位英里每小时)。两小时后它处在 $\left(40,150\right)+150\left[\begin{array}{cc}\frac{1}{2}&\frac{\sqrt{3}}{2}\end{array}\right]+\left[\begin{array}{cc}40&0\end{array}\right]=\left[\begin{array}{cc}155&75\sqrt{3}+150\end{array}\right]=\left[\begin{array}{cc}155.0&279.9\end{array}\right]$

习题 153 Exercise 153

City A is located at the origin $\left(0,0\right)$ while city B is located at $\left(300,500\right)$ where distances are in miles. An airplane flies at $250$ miles per hour in still air. This airplane wants to fly from city A to city B but the wind is blowing in the direction of the positive $y$ axis at a speed of $50$ miles per hour. Find a unit vector such that if the plane heads in this direction, it will end up at city B having flown the shortest possible distance. How long will it take to get there?

城市 A 位于原点 $\left(0,0\right)$ 处,而城市 B 位于 $\left(300,500\right)$,距离单位为英里。一架飞机在静空中以每小时 $250$ 英里飞行。这架飞机想从城市 A 飞到城市 B,但风沿正 $y$ 轴方向以每小时 $50$ 英里吹。求一个单位向量,使飞机朝该方向飞行时,能以最短的可能距离到达城市 B。到达那里要花多长时间?

答案 Answer:Wind: $\left[\begin{array}{cc}0&50\end{array}\right].$ Direction it needs to travel: $\left(3,5\right)\frac{1}{\sqrt{34}}.$ Then you need $250\left[\begin{array}{cc}a&b\end{array}\right]+\left[\begin{array}{cc}0&50\end{array}\right]$ to have this direction where $\left[\begin{array}{cc}a&b\end{array}\right]$ is an appropriate unit vector. Thus you need $\begin{aligned}a^{2}+b^{2}&=1\\ \frac{250b+50}{250a}&=\frac{5}{3}\end{aligned}$ Thus $a=\frac{3}{5}$, $b=\frac{4}{5}.$ The velocity of the plane relative to the ground is $\left[\begin{array}{cc}150&250\end{array}\right].$ The speed of the plane relative to the ground is given by $\sqrt{\left(150\right)^{2}+\left(250\right)^{2}}=291.55$ miles per hour. It has to go a distance of $\sqrt{\left(300\right)^{2}+\left(500\right)^{2}}=583.10$ miles. Therefore, it takes $\frac{583.1}{291.55}=2$ hours.

答案:风:$\left[\begin{array}{cc}0&50\end{array}\right].$ 它需要行进的方向:$\left(3,5\right)\frac{1}{\sqrt{34}}.$ 于是需要 $250\left[\begin{array}{cc}a&b\end{array}\right]+\left[\begin{array}{cc}0&50\end{array}\right]$ 具有这个方向,其中 $\left[\begin{array}{cc}a&b\end{array}\right]$ 是一个合适的单位向量。因此需要 $\begin{aligned}a^{2}+b^{2}&=1\\ \frac{250b+50}{250a}&=\frac{5}{3}\end{aligned}$ 从而 $a=\frac{3}{5}$、$b=\frac{4}{5}.$ 飞机相对地面的速度是 $\left[\begin{array}{cc}150&250\end{array}\right].$ 飞机相对地面的速率由 $\sqrt{\left(150\right)^{2}+\left(250\right)^{2}}=291.55$ 英里每小时给出。它必须飞行 $\sqrt{\left(300\right)^{2}+\left(500\right)^{2}}=583.10$ 英里。因此它需要 $\frac{583.1}{291.55}=2$ 小时。

习题 154 Exercise 154

A certain river is one half mile wide with a current flowing at $2$ miles per hour from East to West. A man swims directly toward the opposite shore from the South bank of the river at a speed of $3$ miles per hour. How far down the river does he find himself when he has swam across? How far does he end up traveling?

某条河宽半英里,水流以每小时 $2$ 英里从东向西流动。一名男子从河的南岸以每小时 $3$ 英里的速度径直游向对岸。当他游过去时,他发现自己被冲下多远?他最终游了多远?

答案 Answer:Water:$\left[\begin{array}{rr}-2&0\end{array}\right]$ Swimmer:$\left[\begin{array}{rr}0&3\end{array}\right]$ Speed relative to earth: $\left[\begin{array}{rr}-2&3\end{array}\right].$ It takes him $1/6$ of an hour to get across. Therefore, he ends up traveling $\frac{1}{6}\sqrt{4+9}=\frac{1}{6}\sqrt{13}$ miles. He ends up $1/3$ mile down stream.

答案:水流:$\left[\begin{array}{rr}-2&0\end{array}\right]$ 游泳者:$\left[\begin{array}{rr}0&3\end{array}\right]$ 相对地面的速度:$\left[\begin{array}{rr}-2&3\end{array}\right].$ 他横渡用了 $1/6$ 小时。因此他最终游了 $\frac{1}{6}\sqrt{4+9}=\frac{1}{6}\sqrt{13}$ 英里。他最终被冲到下游 $1/3$ 英里处。

习题 155 Exercise 155

A certain river is one half mile wide with a current flowing at 2 miles per hour from East to West. A man can swim at $3$ miles per hour in still water. In what direction should he swim in order to travel directly across the river? What would the answer to this problem be if the river flowed at 3 miles per hour and the man could swim only at the rate of 2 miles per hour?

某条河宽半英里,水流以每小时 2 英里从东向西流动。一名男子在静水中能以每小时 $3$ 英里游泳。为了直接横渡这条河,他应朝哪个方向游?若河水流速为每小时 3 英里、而该男子只能以每小时 2 英里游泳,这个问题的答案又会是什么?

答案 Answer:Man: $3\left[\begin{array}{rr}a&b\end{array}\right]$ Water: $\left[\begin{array}{rr}-2&0\end{array}\right]$ Then you need $3a=2$ and so $a=2/3$ and hence $b=\sqrt{5}/3$. The vector is then $\left[\begin{array}{cc}\frac{2}{3}&\frac{\sqrt{5}}{3}\end{array}\right].$

答案:人:$3\left[\begin{array}{rr}a&b\end{array}\right]$ 水流:$\left[\begin{array}{rr}-2&0\end{array}\right]$ 于是需要 $3a=2$,故 $a=2/3$,从而 $b=\sqrt{5}/3$。于是该向量为 $\left[\begin{array}{cc}\frac{2}{3}&\frac{\sqrt{5}}{3}\end{array}\right].$

In the second case, he could not do it. You would need to have a unit vector $\left[\begin{array}{rr}a&b\end{array}\right]$ such that $2a=3$ which is not possible.

第二种情况中,他做不到。你需要一个单位向量 $\left[\begin{array}{rr}a&b\end{array}\right]$ 使得 $2a=3$,这是不可能的。

习题 156 Exercise 156

Three forces are applied to a point which does not move. Two of the forces are $2\vec{i}+2\vec{j}-6\vec{k}$ Newtons and $8\vec{i}+8\vec{j}+3\vec{k}$ Newtons. Find the third force.

三个力作用于一个不动的点。其中两个力是 $2\vec{i}+2\vec{j}-6\vec{k}$ 牛和 $8\vec{i}+8\vec{j}+3\vec{k}$ 牛。求第三个力。

习题 157 Exercise 157

The total force acting on an object is to be $4\vec{i}+2\vec{j}-3\vec{k}$ Newtons. A force of $-3\vec{i}-1\vec{j}+8\vec{k}$ Newtons is being applied. What other force should be applied to achieve the desired total force?

作用在一个物体上的合力应为 $4\vec{i}+2\vec{j}-3\vec{k}$ 牛。现在施加了一个 $-3\vec{i}-1\vec{j}+8\vec{k}$ 牛的力。还应当施加什么力才能达到期望的合力?

习题 158 Exercise 158

A bird flies from its nest $8$ km in the direction $\frac{5}{6}\pi$ north of east where it stops to rest on a tree. It then flies $1$ km in the direction due southeast and lands atop a telephone pole. Place an $xy$ coordinate system so that the origin is the bird's nest, and the positive $x$ axis points east and the positive $y$ axis points north. Find the displacement vector from the nest to the telephone pole.

一只鸟从巢向东偏北 $\frac{5}{6}\pi$ 方向飞出 $8$ 千米,停在树上休息。然后它沿正东南方向飞行 $1$ 千米,落在一根电线杆顶上。建立 $xy$ 坐标系,使原点为鸟巢、正 $x$ 轴指向东、正 $y$ 轴指向北。求从鸟巢到电线杆的位移向量。

习题 159 Exercise 159

If $\vec{F}$ is a force and $\vec{D}$ is a vector, show $\text{proj}_{\vec{D}}\left(\vec{F}\right)=\left(\left\|\vec{F}\right\|\cos\theta\right)\vec{u}$ where $\vec{u}$ is the unit vector in the direction of $\vec{D}$, where $\vec{u}=\vec{D}/\|\vec{D}\|$ and $\theta$ is the included angle between the two vectors, $\vec{F}$ and $\vec{D}$. $\|\vec{F}\|\cos\theta$ is sometimes called the component of the force, $\vec{F}$ in the direction, $\vec{D}$.

若 $\vec{F}$ 是一个力、$\vec{D}$ 是一个向量,证明 $\text{proj}_{\vec{D}}\left(\vec{F}\right)=\left(\left\|\vec{F}\right\|\cos\theta\right)\vec{u}$,其中 $\vec{u}$ 是 $\vec{D}$ 方向上的单位向量,$\vec{u}=\vec{D}/\|\vec{D}\|$,且 $\theta$ 是两向量 $\vec{F}$ 与 $\vec{D}$ 之间的夹角。$\|\vec{F}\|\cos\theta$ 有时被称为力 $\vec{F}$ 在方向 $\vec{D}$ 上的分量。

答案 Answer:$$\text{proj}_{\vec{D}}\left(\vec{F}\right)=\frac{\vec{F}\bullet\vec{D}}{\|\vec{D}\|}\frac{\vec{D}}{\|\vec{D}\|}=\left(\|\vec{F}\|\cos\theta\right)\frac{\vec{D}}{\|\vec{D}\|}=\left(\|\vec{F}\|\cos\theta\right)\vec{u}$$

答案:$$\text{proj}_{\vec{D}}\left(\vec{F}\right)=\frac{\vec{F}\bullet\vec{D}}{\|\vec{D}\|}\frac{\vec{D}}{\|\vec{D}\|}=\left(\|\vec{F}\|\cos\theta\right)\frac{\vec{D}}{\|\vec{D}\|}=\left(\|\vec{F}\|\cos\theta\right)\vec{u}$$

习题 160 Exercise 160

A boy drags a sled for $100$ feet along the ground by pulling on a rope which is $20$ degrees from the horizontal with a force of $40$ pounds. How much work does this force do?

一个男孩沿地面拖雪橇 $100$ 英尺,他用一根与水平方向成 $20$ 度的绳索、以 $40$ 磅的力拉拽。这个力做了多少功?

答案 Answer:$$40\cos\left(\frac{20}{180}\pi\right)100=3758.8$$

答案:$$40\cos\left(\frac{20}{180}\pi\right)100=3758.8$$

习题 161 Exercise 161

A girl drags a sled for $200$ feet along the ground by pulling on a rope which is $30$ degrees from the horizontal with a force of $20$ pounds. How much work does this force do?

一个女孩沿地面拖雪橇 $200$ 英尺,她用一根与水平方向成 $30$ 度的绳索、以 $20$ 磅的力拉拽。这个力做了多少功?

答案 Answer:$$20\cos\left(\frac{\pi}{6}\right)200=3464.1$$

答案:$$20\cos\left(\frac{\pi}{6}\right)200=3464.1$$

习题 162 Exercise 162

A large dog drags a sled for $300$ feet along the ground by pulling on a rope which is $45$ degrees from the horizontal with a force of $20$ pounds. How much work does this force do?

一条大狗沿地面拖雪橇 $300$ 英尺,它用一根与水平方向成 $45$ 度的绳索、以 $20$ 磅的力拉拽。这个力做了多少功?

答案 Answer:$$20\left(\cos\frac{\pi}{4}\right)300=4242.6$$

答案:$$20\left(\cos\frac{\pi}{4}\right)300=4242.6$$

习题 163 Exercise 163

How much work does it take to slide a crate $20$ meters along a loading dock by pulling on it with a $200$ Newton force at an angle of $30^{\circ}$ from the horizontal? Express your answer in Newton meters.

用一个与水平方向成 $30^{\circ}$ 的 $200$ 牛力拉拽,把一只板条箱沿装货码头滑动 $20$ 米需要多少功?用牛顿·米表示你的答案。

答案 Answer:$$200\left(\cos\left(\frac{\pi}{6}\right)\right)20=3464.1$$

答案:$$200\left(\cos\left(\frac{\pi}{6}\right)\right)20=3464.1$$

习题 164 Exercise 164

An object moves $10$ meters in the direction of $\vec{j}$. There are two forces acting on this object, $\vec{F}_{1}=\vec{i}+\vec{j}+2\vec{k}$, and $\vec{F}_{2}=-5\vec{i}+2\vec{j}-6\vec{k}$. Find the total work done on the object by the two forces. Hint: You can take the work done by the resultant of the two forces or you can add the work done by each force. Why?

一个物体沿 $\vec{j}$ 方向移动 $10$ 米。作用在此物体上有两个力 $\vec{F}_{1}=\vec{i}+\vec{j}+2\vec{k}$ 和 $\vec{F}_{2}=-5\vec{i}+2\vec{j}-6\vec{k}$。求这两个力对物体做的总功。提示:你可以取两个力合力做的功,也可以把每个力做的功相加。为什么?

答案 Answer:$\left[\begin{array}{r}-4\\3\\-4\end{array}\right]\bullet\left[\begin{array}{r}0\\1\\0\end{array}\right]\times 10=30$ You can consider the resultant of the two forces because of the properties of the dot product.

答案:$\left[\begin{array}{r}-4\\3\\-4\end{array}\right]\bullet\left[\begin{array}{r}0\\1\\0\end{array}\right]\times 10=30$ 由于点积的性质,你可以考虑两个力的合力。

习题 165 Exercise 165

An object moves $10$ meters in the direction of $\vec{j}+\vec{i}$. There are two forces acting on this object, $\vec{F}_{1}=\vec{i}+2\vec{j}+2\vec{k}$, and $\vec{F}_{2}=5\vec{i}+2\vec{j}-6\vec{k}$. Find the total work done on the object by the two forces. Hint: You can take the work done by the resultant of the two forces or you can add the work done by each force. Why?

一个物体沿 $\vec{j}+\vec{i}$ 方向移动 $10$ 米。作用在此物体上有两个力 $\vec{F}_{1}=\vec{i}+2\vec{j}+2\vec{k}$ 和 $\vec{F}_{2}=5\vec{i}+2\vec{j}-6\vec{k}$。求这两个力对物体做的总功。提示:你可以取两个力合力做的功,也可以把每个力做的功相加。为什么?

答案 Answer:$$\begin{aligned}\vec{F}_{1}\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10+\vec{F}_{2}\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10&=\left(\vec{F}_{1}+\vec{F}_{2}\right)\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10\\ &=\left[\begin{array}{r}6\\4\\-4\end{array}\right]\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10\\ &=50\sqrt{2}\end{aligned}$$

答案:$$\begin{aligned}\vec{F}_{1}\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10+\vec{F}_{2}\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10&=\left(\vec{F}_{1}+\vec{F}_{2}\right)\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10\\ &=\left[\begin{array}{r}6\\4\\-4\end{array}\right]\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10\\ &=50\sqrt{2}\end{aligned}$$

习题 166 Exercise 166

An object moves $20$ meters in the direction of $\vec{k}+\vec{j}$. There are two forces acting on this object, $\vec{F}_{1}=\vec{i}+\vec{j}+2\vec{k}$, and $\vec{F}_{2}=\vec{i}+2\vec{j}-6\vec{k}$. Find the total work done on the object by the two forces. Hint: You can take the work done by the resultant of the two forces or you can add the work done by each force.

一个物体沿 $\vec{k}+\vec{j}$ 方向移动 $20$ 米。作用在此物体上有两个力 $\vec{F}_{1}=\vec{i}+\vec{j}+2\vec{k}$ 和 $\vec{F}_{2}=\vec{i}+2\vec{j}-6\vec{k}$。求这两个力对物体做的总功。提示:你可以取两个力合力做的功,也可以把每个力做的功相加。

答案 Answer:$$\left[\begin{array}{r}2\\3\\-4\end{array}\right]\bullet\left[\begin{array}{r}0\\\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\end{array}\right]20=-10\sqrt{2}$$

答案:$$\left[\begin{array}{r}2\\3\\-4\end{array}\right]\bullet\left[\begin{array}{r}0\\\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\end{array}\right]20=-10\sqrt{2}$$