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
- Find the position vector of a point in $\mathbb{R}^n$.
- 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.
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.
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.
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.
Now suppose $n=3$. You may have previously encountered the $3$-dimensional coordinate system, given by $\mathbb{R}^{3}= \left\{ \left( x_{1}, x_{2}, x_{3}\right) :x_{j}\in \mathbb{R}\text{ for }j=1,2,3 \right\}$.
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.
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.
Consider the following definition.
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 $$\overrightarrow{0P} = \left[ \begin{array}{c} p_{1} \\ \vdots \\ p_{n} \end{array} \right].$$
For this reason we may write both $P=\left( p_{1},\cdots ,p_{n}\right) \in \mathbb{R}^{n}$ and $\overrightarrow{0P} = \left[ p_{1} \cdots p_{n} \right]^T \in \mathbb{R}^{n}$.
This definition is illustrated in the following picture for the special case of $\mathbb{R}^{3}$.
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 $\mathbb{R}^{n}$.
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. $$\overrightarrow{PQ} = \left[ \begin{array}{c} q_{1}-p_{1} \\ \vdots \\ q_{n}-p_{n} \end{array} \right] = \overrightarrow{0Q} - \overrightarrow{0P}.$$
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.
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.
Consider now the general definition for a vector in $\mathbb{R}^n$.
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 $\vec{x}$.
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.
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.
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 $\left[ \begin{array}{r} 2 \\ 3 \end{array} \right] = \left[ \begin{array}{rr} 2 & 3 \end{array} \right]^T$.
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.
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.
向量代数 Vector Algebra
学习目标 Outcomes
- Understand vector addition and scalar multiplication, algebraically.
- Introduce the notion of linear combination of vectors.
- 从代数上理解向量加法与标量乘法。
- 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.
$\mathbb{R}^n$ 中向量的加法 Addition of Vectors in $\mathbb{R}^n$
Addition of vectors in $\mathbb{R}^n$ is defined as follows.
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 $$\begin{aligned} \vec{u}+\vec{v} &= \left[ \begin{array}{c} u_{1} \\ \vdots \\ u_{n} \end{array} \right] + \left[ \begin{array}{c} v_{1} \\ \vdots \\ v_{n} \end{array} \right] \\ & = \left[ \begin{array}{c} u_{1}+v_{1} \\ \vdots \\ u_{n}+v_{n} \end{array} \right]. \end{aligned}$$
To add vectors, we simply add corresponding components. Therefore, in order to add vectors, they must be the same size.
Addition of vectors satisfies some important properties which are outlined in the following theorem.
定理 1:向量加法的性质 Properties of Vector Addition
The following properties hold for vectors $\vec{u},\vec{v}, \vec{w} \in \mathbb{R}^{n}$.
- The Commutative Law of Addition: $\vec{u}+\vec{v}=\vec{v}+\vec{u}$
- The Associative Law of Addition: $\left( \vec{u}+\vec{v}\right) +\vec{w}=\vec{u}+\left( \vec{v}+\vec{w}\right)$
- The Existence of an Additive Identity: $\vec{u}+\vec{0}=\vec{u}$
- The Existence of an Additive Inverse: $\vec{u}+\left( -\vec{u}\right) =\vec{0}$
- 加法交换律:$\vec{u}+\vec{v}=\vec{v}+\vec{u}$
- 加法结合律:$\left( \vec{u}+\vec{v}\right) +\vec{w}=\vec{u}+\left( \vec{v}+\vec{w}\right)$
- 加法单位元的存在性:$\vec{u}+\vec{0}=\vec{u}$
- 加法逆元的存在性:$\vec{u}+\left( -\vec{u}\right) =\vec{0}$
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 $\vec{u}-\vec{v} = \vec{u}+\left( -\vec{v} \right)$.
$\mathbb{R}^n$ 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.
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 $k\vec{u}=k\left[ \begin{array}{c} u_{1} \\ \vdots \\ u_{n} \end{array} \right] = \left[ \begin{array}{c} ku_{1} \\ \vdots \\ ku_{n} \end{array} \right]$.
Just as with addition, scalar multiplication of vectors satisfies several important properties. These are outlined in the following theorem.
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.
- The Distributive Law over Vector Addition: $k \left( \vec{u}+\vec{v}\right) = k\vec{u}+ k\vec{v}$
- The Distributive Law over Scalar Addition: $\left( k + p \right)\vec{u} = k \vec{u}+p \vec{u}$
- The Associative Law for Scalar Multiplication: $k \left( p \vec{u}\right) = \left(k p \right)\vec{u}$
- Rule for Multiplication by $1$: $1\vec{u}=\vec{u}$
- 对向量加法的分配律:$k \left( \vec{u}+\vec{v}\right) = k\vec{u}+ k\vec{v}$
- 对标量加法的分配律:$\left( k + p \right)\vec{u} = k \vec{u}+p \vec{u}$
- 标量乘法结合律:$k \left( p \vec{u}\right) = \left(k p \right)\vec{u}$
- 乘以 $1$ 的规则:$1\vec{u}=\vec{u}$
Proof. We will show the proof of $k \left( \vec{u}+\vec{v}\right) = k \vec{u}+ k \vec{v}$. Note that: $$\begin{array}{ll} k \left( \vec{u}+\vec{v}\right) & =k \left[ u_{1}+v_{1} \cdots u_{n}+v_{n}\right]^T \\ & = \left[ k \left( u_{1}+v_{1}\right) \cdots k \left( u_{n}+v_{n}\right) \right]^T \\ & = \left[ k u_{1}+ k v_{1} \cdots k u_{n}+ k v_{n}\right]^T \\ & = \left[ k u_{1} \cdots k u_{n} \right]^T + \left[ k v_{1} \cdots k v_{n} \right]^T \\ & = k \vec{u}+k \vec{v} \end{array}$$
We now present a useful notion you may have seen earlier combining vector addition and scalar multiplication.
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 $\vec{v} = a_1 \vec{u}_{1} + \cdots + a_n \vec{u}_{n}$.
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 $\vec{u}_{2} = \left[ \begin{array}{r} -3 \\ 0 \\ 1 \end{array} \right]$.
向量加法的几何意义 Geometric Meaning of Vector Addition
学习目标 Learning Objectives
- Understand vector addition, geometrically.
- 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.
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.
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 $\vec{u}+\vec{v}$.
The geometric significance of vector addition in $\mathbb{R}^n$ for any $n$ is given in the following definition.
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 $\vec{u}+\vec{v}$.
This definition is illustrated in the following picture in which $\vec{u}+\vec{v}$ is shown for the special case $n=3$.
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 $\vec{v}$.
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.
例 1:绘制向量加法的图形 Graphing Vector Addition
Consider the following picture of vectors $\vec{u}$ and $\vec{v}$.
Sketch a picture of $\vec{u}+\vec{v}$ and $\vec{u}-\vec{v}.$
解 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 $\vec{v}$.
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.
向量的长度 Length of a Vector
学习目标 Learning Objectives
- Find the length of a vector and the distance between two points in $\mathbb{R}^n$.
- Find the corresponding unit vector to a vector in $\mathbb{R}^n$.
- 求 $\mathbb{R}^n$ 中向量的长度以及两点之间的距离。
- 求 $\mathbb{R}^n$ 中某个向量对应的单位向量。
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 $\mathbb{R}^n$.
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}} $.
Consider now the case for $n=2$, demonstrated by the following picture.
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 $\left( \left| p_{1}-q_{1}\right| ^{2}+\left| p_{2}-q_{2}\right| ^{2}\right) ^{1/2}=\left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}\right) ^{1/2}$.
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 $\left( p_{1},p_{2},q_{3}\right) .$
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 $\left( \left( \left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}\right) ^{1/2}\right) ^{2}+\left( p_{3}-q_{3}\right) ^{2}\right) ^{1/2}= \left( \left( p_{1}-q_{1}\right) ^{2}+\left( p_{2}-q_{2}\right) ^{2}+\left( p_{3}-q_{3}\right) ^{2}\right) ^{1/2}$.
This discussion motivates the following definition for the distance between points in $\mathbb{R}^n$.
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 $Q$.
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 $\mathbb{R}^4$.
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 $Q=\left( 2,3,-1,0\right)$.
解 Solution
We will use the formula given in Definition 1 to find the distance between $P$ and $Q$. Use the distance formula and write $d(P,Q)= \left( \left( 1-2\right) ^{2}+\left( 2-3\right) ^{2}+\left( -4-\left( -1\right) \right) ^{2}+\left( 6-0\right)^{2}\right) ^{\frac{1}{2}} = 47.$
Therefore, $d( P,Q) = \sqrt{47}.$
There are certain properties of the distance between points which are important in our study. These are outlined in the following theorem.
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.
- $d( P, Q) = d( Q, P)$
- $d( P, Q) \geq 0$, and equals $0$ exactly when $P = Q.$
- $d( P, Q) = d( Q, P)$
- $d( P, Q) \geq 0$,且当且仅当 $P = Q$ 时等于 $0$。
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.
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 $\left( 0,1,2\right) .$
解 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 $2p_1+2p_2+2p_3=-9$.
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.
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 $\| \vec{u} \| = \sqrt{ u_{1}^2 + \cdots + u_{n}^2}$.
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, $d(P,Q)=\| \overrightarrow{PQ} \|$.
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 $\sqrt{47}$.
We conclude this section with a new definition for the special case of vectors of length $1$.
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 $\| \vec{u} \| = 1$.
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 $\vec{u} = \frac{1}{\| \vec{v} \|} \vec{v}$.
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.
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 $\vec{v}$.
解 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 $$\begin{aligned} \vec{u} &= \frac{1}{\| \vec{v} \|} \vec{v} \\ &= \frac{1}{\sqrt{26}} \left[ \begin{array}{rrr} 1 & -3 & 4 \end{array} \right]^T \\ &= \left[ \begin{array}{rrr} \frac{1}{\sqrt{26}} & -\frac{3}{\sqrt{26}} & \frac{4}{\sqrt{26}} \end{array} \right]^T \end{aligned}$$
You can verify using the Definition 1 that $\| \vec{u} \| = 1$.
标量乘法的几何意义 Geometric Meaning of Scalar Multiplication
学习目标 Outcomes
- Understand scalar multiplication, geometrically.
- 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.
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, $\left| k \right| >0$.
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 $k > 0$.
Consider the following example.
例 1:绘制标量乘法的图形 Graphing Scalar Multiplication
Consider the vectors $\vec{u}$ and $\vec{v}$ drawn below.
Draw $-\vec{u}$, $2\vec{v}$, and $-\frac{1}{2}\vec{v}$.
解 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.
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.
In the following example, we examine the geometric meaning of this concept.
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 $\vec{v}$.
Sketch a picture of $\vec{u}+2\vec{v},\vec{u}-\frac{1}{2}\vec{v}.$
解 Solution
The two vectors are shown below.
参数直线 Parametric Lines
学习目标 Outcomes
- Find the vector and parametric equations of a line.
- 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 $\mathbb{R}^3$.
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.
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.
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, $\vec{p} = \vec{p_0} + (\vec{p} - \vec{p_0})$.
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 $\vec{q} = \vec{p_0} + t \left( \vec{p}- \vec{p_0}\right)$.
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.
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 $t\in \mathbb{R}$.
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 $\vec{p}=\vec{p_0}+t\vec{d},\; t\in\mathbb{R}$.
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.
The following theorem claims that such an equation is in fact a line.
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.
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.
We can use the above discussion to find the equation of a line when given two distinct points. Consider the following example.
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 $P = \left( 2,-4,6\right).$
解 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.
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.
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.
Consider the following example.
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 $\vec{d} = \left[ \begin{array}{c} 1 \\ 2 \\ 1 \end{array} \right]^B$.
解 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 $\left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B = \left[ \begin{array}{c} 1 \\ 2 \\ 0 \end{array} \right]^B + t \left[ \begin{array}{c} 1 \\ 2 \\ 1 \end{array} \right]^B, \; t\in \mathbb{R}$.
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.
Consider the following definition.
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 $L$.
You can verify that the form discussed following Example 2 has the form given in Definition 2.
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.
In the following example, we look at how to take the equation of a line from symmetric form to parametric form.
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.
解 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.
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 $\left[ \begin{array}{c} x \\ y \\ z \end{array} \right]^B = \left[ \begin{array}{r} 2 \\ 1 \\ -3 \end{array} \right]^B + t \left[ \begin{array}{r} 3 \\ 2 \\ 1 \end{array} \right]^B, \; t\in \mathbb{R}$.
点积 The Dot Product
学习目标 Outcomes
- Compute the dot product of vectors, and use this to compute vector projections.
- 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.
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 $\vec{u}\bullet \vec{v} = \sum_{k=1}^{n}u_{k}v_{k}$.
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 $\vec{u}\vec{v}^{T}$.
Consider the following example.
例 1:计算一个点积 Compute a Dot Product
Find $\vec{u} \bullet \vec{v}$ for $\vec{u} = \left[ \begin{array}{r} 1 \\ 2 \\ 0 \\ -1 \end{array} \right],\; \vec{v} = \left[ \begin{array}{r} 0 \\ 1 \\ 2 \\ 3 \end{array} \right]$.
解 Solution
By Definition 1, we must compute $\vec{u}\bullet \vec{v} = \sum_{k=1}^{4}u_{k}v_{k}$. This is given by $$\begin{aligned} \vec{u} \bullet \vec{v} &= (1)(0) + (2)(1) + (0)(2) + (-1)(3) \\ &= 0 + 2 + 0 + -3 \\ &= -1. \end{aligned}$$
With this definition, there are several important properties satisfied by the dot product.
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.
- $\vec{u}\bullet \vec{v}= \vec{v}\bullet \vec{u}$
- $\vec{u}\bullet \vec{u}\geq 0$ and equals zero if and only if $\vec{u}=\vec{0}$
- $\left( k\vec{u}+p\vec{v}\right) \bullet \vec{w}=k\left( \vec{u}\bullet \vec{w}\right) +p\left( \vec{v}\bullet \vec{w}\right)$
- $\vec{u}\bullet\left( k\vec{v}+p\vec{w}\right) =k\left( \vec{u}\bullet \vec{v}\right) +p\left( \vec{u}\bullet \vec{w}\right)$
- $\| \vec{u}\|^{2}=\vec{u}\bullet \vec{u}$
- $\vec{u}\bullet \vec{v}= \vec{v}\bullet \vec{u}$
- $\vec{u}\bullet \vec{u}\geq 0$,且当且仅当 $\vec{u}=\vec{0}$ 时等于 $0$
- $\left( k\vec{u}+p\vec{v}\right) \bullet \vec{w}=k\left( \vec{u}\bullet \vec{w}\right) +p\left( \vec{v}\bullet \vec{w}\right)$
- $\vec{u}\bullet\left( k\vec{v}+p\vec{w}\right) =k\left( \vec{u}\bullet \vec{v}\right) +p\left( \vec{u}\bullet \vec{w}\right)$
- $\| \vec{u}\|^{2}=\vec{u}\bullet \vec{u}$
Proof. The proof is left as an exercise.
This proposition tells us that we can also use the dot product to find the length of a vector.
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 $\| \vec{u} \|.$
解 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, $$\begin{aligned} \| \vec{u} \| &= \sqrt {\vec{u} \bullet \vec{u}} \\ &= \sqrt{25} \\ &= 5. \end{aligned}$$
You may wish to compare this to our previous definition of length, given in Definition 4.4.2.
The Cauchy Schwarz inequality is a fundamental inequality satisfied by the dot product. It is given in the following theorem.
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.
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 $\vec{v}\neq \vec{0}$.
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, $$\begin{aligned} f\left( t\right) &=\vec{u}\bullet \left( \vec{u}+t\vec{v}\right) + t\vec{v}\bullet \left( \vec{u}+t\vec{v}\right) \\ &=\vec{u}\bullet \vec{u}+t\left( \vec{u}\bullet \vec{v}\right) + t \vec{v}\bullet \vec{u}+ t^{2}\vec{v}\bullet \vec{v} \\ &=\| \vec{u}\|^{2}+2t\left( \vec{u}\bullet \vec{v}\right) +\| \vec{v}\|^{2}t^{2}. \end{aligned}$$
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 $\left\vert \vec{u}\bullet \vec{v} \right\vert <\| \vec{u}\| \| \vec{v}\|$.
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 $\mathbb{R}^{n}$.
The Cauchy Schwarz inequality provides another proof of the triangle inequality for distances in $\mathbb{R}^{n}$.
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.
Also $\left\| \| \vec{u}\| -\| \vec{v}\| \right\| \leq \| \vec{u}-\vec{v}\|$.
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.
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, $k \geq 0.$
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.
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.
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. $\vec{u}\bullet \vec{v}=\| \vec{u}\| \| \vec{v}\| \cos \theta$.
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.
Consider the following example.
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 $\vec{u} = \left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right],\; \vec{v} = \left[ \begin{array}{r} 3 \\ 4 \\ 1 \end{array} \right]$.
解 Solution
By Proposition 2, $\vec{u}\bullet \vec{v}=\| \vec{u}\| \| \vec{v}\| \cos \theta$. Hence, $\cos \theta =\frac{\vec{u}\bullet \vec{v}}{\| \vec{u}\| \| \vec{v}\|}$.
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 $\cos \theta =\frac{9}{\sqrt{26}\sqrt{6}}=0.7205766...$.
With the cosine known, the angle can be determined by computing the inverse cosine of that angle, giving approximately $\theta =0.76616$ radians.
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.
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 $L_2: \left[ \begin{array}{r} x \\ y \\ z \end{array} \right] = \left[ \begin{array}{r} 0 \\ 4 \\ -3 \end{array} \right] +s\left[ \begin{array}{r} 2 \\ 1 \\ -1 \end{array} \right]$.
解 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.
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 $\theta = \frac{2 \pi}{3}$.
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 $\frac{\pi}{3}$.
We can also use Proposition 2 to compute the dot product of two vectors.
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 $\vec{u}\bullet \vec{v}$.
解 Solution
From the geometric description of the dot product in Proposition 2, $\vec{u}\bullet \vec{v}=(3)(4) \cos \left( \pi / 3\right) =3\times 4\times 1/2=6$.
Two nonzero vectors are said to be perpendicular, sometimes also called orthogonal, if the included angle is $\pi /2$ radians ($90^{\circ }$).
Consider the following proposition.
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 $\vec{u} \bullet \vec{v} = 0$.
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.
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 $\vec{u} \bullet \vec{v} = 0$.
Consider the following example.
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.
解 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.
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.
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 $\vec{u}$.
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.
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 $$\begin{aligned} \vec{v}_{\bot }\bullet \vec{u} &= \vec{v}\bullet \vec{u}-\frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}\vec{u}\bullet \vec{u} \\ &= \vec{v}\bullet\vec{u}-\vec{v}\bullet \vec{u} \\ &= 0. \end{aligned}$$
The vector $\vec{v}_{\|}$ in Theorem 3 is called the projection of $\vec{v}$ onto $\vec{u}$ and is denoted by $\vec{v}_{\|} = \mathrm{proj}_{\vec{u}}\left( \vec{v}\right)$.
We now make a formal definition of the vector projection.
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 $\mathrm{proj}_{\vec{u}}\left( \vec{v}\right) =\left( \frac{\vec{v}\bullet \vec{u}}{\vec{u}\bullet \vec{u}}\right) \vec{u} = \frac{\vec{v}\bullet \vec{u}}{\| \vec{u}\|^{2}}\vec{u}$.
Consider the following example of a projection.
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 $\vec{u}= \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right],\; \vec{v}= \left[ \begin{array}{r} 1 \\ -2 \\ 1 \end{array} \right]$.
解 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 $$\begin{aligned} \mathrm{proj}_{\vec{u}}\left( \vec{v}\right) &=-\frac{8}{29} \left[ \begin{array}{r} 2 \\ 3 \\ -4 \end{array} \right] \\ &= \left[ \begin{array}{r} - \frac{16}{29} \\ - \frac{24}{29} \\ \frac{32}{29} \end{array} \right]. \end{aligned}$$
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 $L$.
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 $P$.
解 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 $\left[ \begin{array}{r} 1 \\ 3 \\ 5 \end{array} \right] - \left[ \begin{array}{r} 0 \\ 4 \\ -2 \end{array} \right] = \left[ \begin{array}{r} 1 \\ -1 \\ 7 \end{array} \right]$.
Then, if $Q$ is the point on $L$ closest to $P$, it follows that $$\begin{aligned} \overrightarrow{P_0Q} &= \mathrm{proj}_{\vec{d}}\overrightarrow{P_0P} \\ &= \left( \frac{ \overrightarrow{P_0P}\bullet \vec{d}}{\| \vec{d}\|^2}\right) \vec{d} \\ &= \frac{15}{9} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right] \\ &= \frac{5}{3} \left[ \begin{array}{r} 2 \\ 1 \\ 2 \end{array} \right]. \end{aligned}$$
Now, the distance from $P$ to $L$ is given by $\| \overrightarrow{QP} \| = \| \overrightarrow{P_0P} - \overrightarrow{P_0Q}\| = \sqrt{26}$.
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, $Q = (\frac{10}{3}, \frac{17}{3}, \frac{4}{3})$.
平面 Planes in $\mathbb{R}^n$
学习目标 Outcomes
- Find the vector and scalar equations of a plane.
- 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.
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.
In other words, we say that $\vec{n}$ is orthogonal (perpendicular) to every vector in the plane.
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 $\vec{n} \bullet \overrightarrow{P_0P} = 0$.
The first of these equations gives the vector equation of the plane.
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 $\vec{n} \bullet (\overrightarrow{0P} - \overrightarrow{0P_0}) = 0$.
Notice that this equation can be used to determine if a point $P$ is contained in a certain plane.
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.
解 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.
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 $ax + by + cz = d$.
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 $d = ax_0 + by_0 + cz_0$.
Consider the following equation.
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 $\vec{n} = \left[ \begin{array}{r} -2 \\ 4 \\ 1 \end{array} \right]$.
解 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. $-2x + 4y + 1z = -2(3) + 4(-2) + 1(5) = -9$.
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 $-2x + 4y + 1z = -9$.
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.
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 $P$.
解 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 $Q$.
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, $\overrightarrow{P_0P} = \left[ \begin{array}{c} 3 \\ 2 \\ 3 \end{array} \right] - \left[ \begin{array}{c} 1 \\ 0 \\ 0 \end{array} \right] = \left[ \begin{array}{c} 2 \\ 2 \\ 3 \end{array} \right]$.
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 $4$.
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, $Q = (\frac{1}{3}, \frac{2}{3}, \frac{1}{3} )$.
叉积 The Cross Product
学习目标 Outcomes
- Compute the cross product and box product of vectors in $\mathbb{R}^3$.
- 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.
Consider the following definition.
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 $\vec{w}$.
For an example of a right handed system of vectors, see the following picture.
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 $\vec{w}$.
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.
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 $\vec{k}$.
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.
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.
- Its length is $\| \vec{u}\times \vec{v}\| =\| \vec{u}\| \| \vec{v}\| \sin \theta,$ where $\theta$ is the included angle between $\vec{u}$ and $\vec{v}$.
- 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.
- 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。
- 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.
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 $\vec{u}=u_{1}\vec{i}+u_{2}\vec{j}+u_{3}\vec{k}$.
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 $$\vec{u} \times \vec{v} = \left[ \begin{array}{r} u_{2}v_{3}-u_{3}v_{2} \\ -(u_{1}v_{3}-u_{3}v_{1}) \\ u_{1}v_{2}-u_{2}v_{1} \end{array} \right].$$
We now prove this proposition.
证明 Proof
From the above table and the properties of the cross product listed, $$\begin{aligned} \vec{u} \times \vec{v} &= \left( u_{1}\vec{i}+u_{2}\vec{j}+u_{3}\vec{k}\right) \times \left( v_{1}\vec{i}+v_{2}\vec{j}+v_{3}\vec{k}\right) \\ &= u_{1}v_{2}\vec{i}\times \vec{j}+u_{1}v_{3}\vec{i}\times \vec{k}+u_{2}v_{1}\vec{j}\times \vec{i}+ u_{2}v_{3}\vec{j}\times \vec{k}+ +u_{3}v_{1}\vec{k}\times \vec{i}+u_{3}v_{2}\vec{k}\times \vec{j} \\ &=u_{1}v_{2}\vec{k}-u_{1}v_{3}\vec{j}-u_{2}v_{1}\vec{k}+u_{2}v_{3} \vec{i}+u_{3}v_{1}\vec{j}-u_{3}v_{2}\vec{i} \\ &=\left( u_{2}v_{3}-u_{3}v_{2}\right) \vec{i}+\left( u_{3}v_{1}-u_{1}v_{3}\right) \vec{j}+\left( u_{1}v_{2}-u_{2}v_{1}\right) \vec{k} \end{aligned}$$
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.
The cross product satisfies the following properties.
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.
- $\vec{u}\times \vec{v}= -\left( \vec{v}\times \vec{u}\right), \mbox{ and } \vec{u}\times \vec{u}=\vec{0}$
- $\left( k \vec{u}\right)\times \vec{v}= k \left( \vec{u}\times \vec{v}\right) =\vec{u}\times \left( k \vec{v}\right)$
- $\vec{u}\times \left( \vec{v}+\vec{w}\right) =\vec{u}\times \vec{v}+\vec{u}\times \vec{w}$
- $\left( \vec{v}+\vec{w}\right) \times \vec{u}=\vec{v} \times \vec{u}+\vec{w}\times \vec{u}$
- $\vec{u}\times \vec{v}= -\left( \vec{v}\times \vec{u}\right), \quad \text{且 } \vec{u}\times \vec{u}=\vec{0}$
- $\left( k \vec{u}\right)\times \vec{v}= k \left( \vec{u}\times \vec{v}\right) =\vec{u}\times \left( k \vec{v}\right)$
- $\vec{u}\times \left( \vec{v}+\vec{w}\right) =\vec{u}\times \vec{v}+\vec{u}\times \vec{w}$
- $\left( \vec{v}+\vec{w}\right) \times \vec{u}=\vec{v} \times \vec{u}+\vec{w}\times \vec{u}$
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.
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.
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 $1.$, $$\begin{aligned} \left( \vec{v}+\vec{w}\right) \times \vec{u}& =-\vec{u}\times \left( \vec{v}+\vec{w}\right) \\ & =-\left( \vec{u}\times \vec{v}+\vec{u}\times \vec{w}\right) \\ & =\vec{v}\times \vec{u}+\vec{w}\times \vec{u}. \end{aligned}$$
We will now look at an example of how to compute a cross product.
例 1:求一个叉积 Find a Cross Product
Find $\vec{u} \times \vec{v}$ for the following vectors $$\vec{u} = \left[ \begin{array}{r} 1 \\ -1 \\ 2 \end{array} \right], \vec{v} = \left[ \begin{array}{r} 3 \\ -2 \\ 1 \end{array} \right].$$
解 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 $\vec{u} \times \vec{v} = \left[ \begin{array}{r} 3 \\ 5 \\ 1 \end{array} \right]$.
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.
We examine this concept in the following example.
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 $$\vec{u} = \left[ \begin{array}{r} 1 \\ -1 \\ 2 \end{array} \right], \vec{v} = \left[ \begin{array}{r} 3 \\ -2 \\ 1 \end{array} \right].$$
解 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 $$\| \vec{u} \times \vec{v} \| = \sqrt{(3)(3) + (5)(5) + (1)(1)} = \sqrt{9+25+1}=\sqrt{35}.$$
We can also use this concept to find the area of a triangle. Consider the following example.
例 3:三角形的面积 Area of Triangle
Find the area of the triangle determined by the points $\left(1, 2, 3 \right) , \left( 0,2,5\right), \left( 5,1, 2 \right)$.
解 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 $\frac{1}{2}\sqrt{54}= \frac{3}{2}\sqrt{6}.$
In general, if you have three points in $\mathbb{R}^{3}, P,Q,R$, the area of the triangle is given by $\frac{1}{2}\| \vec{PQ} \times \vec{PR} \|$.
Recall that $\vec{PQ}$ is the vector running from point $P$ to point $Q$.
In the next section, we explore another application of the cross product.
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.
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 $\left\{ r\vec{u}+s\vec{v}+t\vec{w}:r,s,t\in \left[ 0,1\right] \right\}$.
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.
The following is an example of a parallelepiped.
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.
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 $\left\| \left(\vec{u}\times\vec{v}\right) \cdot \vec{w} \right\|$.
Consider an example of this concept.
例 4:平行六面体的体积 Volume of a Parallelepiped
Find the volume of the parallelepiped determined by the vectors $$\vec{u} = \left[ \begin{array}{r} 1 \\ 2 \\ -5 \end{array} \right], \vec{v} = \left[ \begin{array}{r} 1 \\ 3 \\ -6 \end{array} \right], \vec{w} = \left[ \begin{array}{r} 3 \\ 2 \\ 3 \end{array} \right].$$
解 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.
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.
There is a fundamental observation which comes directly from the geometric definitions of the cross product and the dot product.
命题 3:乘积的次序 Order of the Product
Let $\vec{u},\vec{v}$, and $\vec{w}$ be vectors. Then $\left( \vec{u}\times \vec{v}\right) \cdot \vec{w}=\vec{u}\cdot \left( \vec{v}\times \vec{w} \right) .$
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.
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. $$\begin{aligned} \vec{u}\cdot \left(\vec{v}\times \vec{w}\right) &= \left[ \begin{array}{r} a \\ b \\ c \end{array} \right] \cdot \left\| \begin{array}{rrr} \vec{i} & \vec{j} & \vec{k} \\ d & e & f \\ g & h & i \end{array} \right\| \\ &=a\left\| \begin{array}{rr} e & f \\ h & i \end{array} \right\| -b\left\| \begin{array}{rr} d & f \\ g & i \end{array} \right\| +c\left\| \begin{array}{rr} d & e \\ g & h \end{array} \right\| \\ &= \det \left[ \begin{array}{rrr} a & b & c \\ d & e & f \\ g & h & i \end{array} \right]. \end{aligned}$$
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.
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.
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.
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.
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 $\mathbb{R}^{n}$.
I:构造一组新向量
I: Construct a new set of vectors $\vec{v}_{1}, \ldots, \vec{v}_{n}$ as follows:
$$\begin{array}{ll} \vec{v}_{1} & = \vec{u}_{1} \\ \vec{v}_{2} & = \vec{u}_{2} - \left( \dfrac{ \vec{u}_2 \cdot \vec{v}_1}{ \vec{v}_1^2} \right) \vec{v}_1 \\ \vec{v}_{3} & = \vec{u}_{3} - \left( \dfrac{\vec{u}_3 \cdot \vec{v}_1}{ \vec{v}_1^2} \right) \vec{v}_1 - \left( \dfrac{\vec{u}_3 \cdot \vec{v}_2}{ \vec{v}_2^2} \right) \vec{v}_2 \\ \vdots \\ \vec{v}_{n} & = \vec{u}_{n} - \left( \dfrac{\vec{u}_n \cdot \vec{v}_1}{ \vec{v}_1^2} \right) \vec{v}_1 - \left( \dfrac{\vec{u}_n \cdot \vec{v}_2}{ \vec{v}_2^2} \right) \vec{v}_2 - \cdots - \left( \dfrac{\vec{u}_n \cdot \vec{v}_{n-1}}{ \vec{v}_{n-1}^2} \right) \vec{v}_{n-1} \end{array}$$
II:归一化
II: Now let $\vec{w}_{i} = \dfrac{\vec{v}_{i}}{ \vec{v}_i }$ for $i=1, \cdots ,n$.
Then
1. $\left\{ \vec{v}_{1}, \cdots, \vec{v}_{n} \right\}$ is an orthogonal set.
2. $\left\{ \vec{w}_{1}, \cdots, \vec{w}_{n} \right\}$ is an orthonormal set.
3. $\mathrm{span}\left\{ \vec{u}_{1}, \cdots ,\vec{u}_{n} \right\} = \mathrm{span} \left\{ \vec{v}_{1}, \cdots, \vec{v}_{n} \right\} = \mathrm{span}\left\{ \vec{w}_{1}, \cdots ,\vec{w}_{n} \right\}.$
解 Solution
The full proof of this algorithm is beyond this material, however here is an indication of the arguments.
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:
$$\begin{array}{ll} \vec{v}_{1} \cdot \vec{v}_{2} & = \vec{v}_{1} \cdot \left( \vec{u}_{2} - a_2 \vec{v}_{1} \right) \\ & = \vec{v}_{1} \cdot \vec{u}_{2} - a_2 (\vec{v}_{1} \cdot \vec{v}_{1}) \\ & = \vec{v}_{1} \cdot \vec{u}_{2} - \dfrac{ \vec{u}_{2} \cdot \vec{v}_{1}}{ \vec{v}_1^2} \vec{v}_{1}^2 \\ & = ( \vec{v}_{1} \cdot \vec{u}_{2} ) - ( \vec{u}_{2} \cdot \vec{v}_{1} ) = 0 \end{array}$$
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.
Then in a similar fashion you show that $\mathrm{span}\left\{ \vec{u}_{1},\cdots ,\vec{u}_{n} \right\} = \mathrm{span}\left\{ \vec{v}_{1},\cdots ,\vec{v}_{n} \right\}.$
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.
Consider the following example.
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 $\vec{u}_{1}=\left[ \begin{array}{r} 1 \\ 1 \\ 0 \end{array} \right], \vec{u}_{2}=\left[ \begin{array}{r} 3 \\ 2 \\ 0 \end{array} \right] \in \mathbb{R}^{3}$.
Use the Gram-Schmidt algorithm to find an orthonormal set of vectors $\{\vec{w}_{1}, \vec{w}_{2}\}$ having the same span.
解 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:
$$\begin{aligned} \vec{v}_{1} &= \vec{u}_{1} = \left[ \begin{array}{r} 1 \\ 1 \\ 0 \end{array} \right] \\ \vec{v}_{2} &= \vec{u}_{2} - \left( \dfrac{\vec{u}_2 \cdot \vec{v}_{1}}{ \vec{v}_1^2} \right) \vec{v}_{1} \\ &= \left[ \begin{array}{r} 3 \\ 2 \\ 0 \end{array} \right] - \frac{5}{2} \left[ \begin{array}{r} 1 \\ 1 \\ 0 \end{array} \right] \\ &= \left[ \begin{array}{r} \frac{1}{2} \\ - \frac{1}{2} \\ 0 \end{array} \right] \end{aligned}$$
Now to normalize simply let
$$\begin{aligned} \vec{w}_{1} &= \frac{\vec{v}_{1}}{ \vec{v}_1 } = \left[ \begin{array}{r} \frac{1}{\sqrt{2}} \\ \frac{1}{\sqrt{2}} \\ 0 \end{array} \right] \\ \vec{w}_{2} &= \frac{\vec{v}_{2}}{ \vec{v}_2 } = \left[ \begin{array}{r} \frac{1}{\sqrt{2}} \\ - \frac{1}{\sqrt{2}} \\ 0 \end{array} \right] \end{aligned}$$
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.
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.
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 $U$.
解 Solution
First $\vec{f}_{1}=\vec{x}_{1}$.
Next, $\vec{f}_{2}=\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 1 \end{array}\right] -\frac{2}{2}\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right] =\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right].$
Finally, $\vec{f}_{3}=\left[\begin{array}{c} 1 \\ 1 \\ 0 \\ 0 \end{array}\right] -\frac{1}{2}\left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right] -\frac{0}{1}\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right] =\left[\begin{array}{c} \frac{1}{2} \\ 1 \\ -\frac{1}{2} \\ 0 \end{array}\right].$
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 $B=\left\{ \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right], \left[\begin{array}{r} 1 \\ 2 \\ -1 \\ 0 \end{array}\right] \right\}.$
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.
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 $\mathbb{R}^n$.
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.
The vector $\vec{z}$ is called the orthogonal projection of $\vec{y}$ on $W$. The definition is given as follows.
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 $W$.
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.
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 $Y$.
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 $W$.
Then, $Z$ is the point in $W$ closest to $Y$.
证明
Proof. First $Z$ is certainly a point in $W$ since it is in the span of a basis of $W$.
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 $\|\vec{z} -\vec{z}_1\|^2 > 0$.
Hence, $\|\vec{y} - \vec{z}_1\|^2 > \|\vec{y} - \vec{z}\|^2$. Taking the square root of each side, we obtain the desired result.
Consider the following example.
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 $Y = (1,0,3)$.
解 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}$.
We can thus write $W$ as
$$\begin{aligned} W &= \mbox{span} \left\{ \vec{u}_{1}, \vec{u}_{2} \right\} \\ &= \mbox{span} \left\{ \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right], \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right] \right\} \end{aligned}$$
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.
$$\vec{w}_{1} = \vec{u}_{1} = \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right] \\ \begin{aligned} \vec{w}_{2} &= \vec{u}_{2} - \left( \frac{ \vec{u}_{2} \cdot \vec{w}_{1}}{ \vec{w}_{1}^2} \right) \vec{w}_{1} \\ &= \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right] - \left( \frac{-2}{2}\right) \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right] \\ &= \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right] + \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right] \\ &= \left[ \begin{array}{c} 1 \\ 1 \\ 1 \end{array} \right] \end{aligned}$$
Therefore an orthogonal basis of $W$ is $\left\{ \vec{w}_{1}, \vec{w}_{2} \right\} = \left\{ \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right], \left[ \begin{array}{c} 1 \\ 1 \\ 1 \end{array} \right] \right\}$.
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:
$$\begin{aligned} \vec{z} &= \mathrm{proj}_{W}\left( \vec{y}\right) \\ &= \left( \frac{\vec{y} \cdot \vec{w}_1}{ \vec{w}_1^2}\right) \vec{w}_1 + \left( \frac{\vec{y} \cdot \vec{w}_2}{ \vec{w}_2^2}\right) \vec{w}_2 \\ &= \left( \frac{-2}{2} \right) \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right] + \left( \frac{4}{3} \right) \left[ \begin{array}{c} 1 \\ 1 \\ 1 \end{array} \right] \\ &= \left[ \begin{array}{c} \frac{1}{3} \\ \frac{4}{3} \\ \frac{7}{3} \end{array} \right] \end{aligned}$$
Therefore the point $Z$ on $W$ closest to the point $(1,0,3)$ is $\left( \frac{1}{3}, \frac{4}{3}, \frac{7}{3} \right)$.
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 $W^{\perp}$.
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 $W$. $W^{\perp} = \{ \vec{x} \in \mathbb{R}^n \mbox{ such that } \vec{x} \cdot \vec{z} = 0 \mbox{ for all } \vec{z} \in W \}$
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.
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.
The following proposition demonstrates that the orthogonal complement of a subspace is itself a subspace.
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 $\mathbb{R}^n$.
Consider the following proposition.
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, $\left\{ \vec{0} \right\}^{\perp} = (\mathbb{R}^n)$.
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., $\{\vec{0}\}^{\perp}=\mathbb{R}^n$.
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 $(\mathbb{R}^n)^{\perp}=\{\vec{0}\}$.
In the next example, we will look at how to find $W^{\perp}$.
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 $W$.
解 Solution
From Example 11 we know that we can write $W$ as $W = \mbox{span} \left\{ \vec{u}_{1}, \vec{u}_{2} \right\} = \mbox{span} \left\{ \left[ \begin{array}{r} 1 \\ 0 \\ -1 \end{array} \right], \left[ \begin{array}{c} 0 \\ 1 \\ 2 \end{array} \right] \right\}$.
In order to find $W^{\perp}$, we need to find all $\vec{x}$ which are orthogonal to every vector in this span.
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. $x_1 - x_3 = 0$
In order to satisfy $\vec{x} \cdot \vec{u}_{2} = 0$, the following equation must hold. $x_2 + 2x_3 = 0$
Both of these equations must be satisfied, so we have the following system of equations.
$$\begin{array}{c} x_1 - x_3 = 0 \\ x_2 + 2x_3 = 0 \end{array}$$
To solve, set up the augmented matrix.
$$\left[ \begin{array}{rrr|r} 1 & 0 & -1 & 0 \\ 0 & 1 & 2 & 0 \end{array} \right]$$
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 $W^{\perp}$.
The following results summarize the important properties of the orthogonal projection.
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,
1. The position vector $\vec{z}$ of the point $Z$ is given by $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right)$
2. $\vec{z} \in W$ and $\vec{y} - \vec{z} \in W^{\perp}$
3. $\| Y - Z \| < \| Y - Z_1 \|$ for all $Z_1 \neq Z \in W$
Consider the following example of this concept.
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 $\vec{y}$.
解 Solution
We will first use the Gram-Schmidt Process to construct the orthogonal basis, $B$, of $W$: $B=\left\{ \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 1 \end{array}\right], \left[\begin{array}{r} 1 \\ 2 \\ -1 \\ 0 \end{array}\right] \right\}.$
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 $\vec{y}$.
Consider the next example.
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 $W^{\perp}$.
解 Solution
From Theorem 3 the point $Z$ in $W$ closest to $Y$ is given by $\vec{z} = \mathrm{proj}_{W}\left( \vec{y}\right)$.
Notice that since the above vectors already give an orthogonal basis for $W$, we have:
$$\begin{aligned} \vec{z} &= \mathrm{proj}_{W}\left( \vec{y}\right) \\ &= \left( \frac{\vec{y} \cdot \vec{w}_{1}}{ \vec{w}_{1}^2}\right) \vec{w}_{1} + \left( \frac{\vec{y} \cdot \vec{w}_{2}}{ \vec{w}_{2}^2}\right) \vec{w}_{2} \\ &= \left( \frac{4}{2} \right) \left[ \begin{array}{c} 1 \\ 0 \\ 1 \\ 0 \end{array} \right] + \left( \frac{10}{5} \right) \left[ \begin{array}{c} 0 \\ 1 \\ 0 \\ 2 \end{array} \right] \\ &= \left[ \begin{array}{c} 2 \\ 2 \\ 2 \\ 4 \end{array} \right] \end{aligned}$$
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 $\left[ \begin{array}{c} 1 \\ 2 \\ 3 \\ 4 \end{array} \right] = \left[ \begin{array}{c} 2 \\ 2 \\ 2 \\ 4 \end{array} \right] + \left[ \begin{array}{r} -1 \\ 0 \\ 1 \\ 0 \end{array} \right]$
Example 15: Point in a Plane Closest to a Given Point
Find the point $Z$ in the plane $3x+y-2z=0$ that is closest to the point $Y=(1,1,1)$.
解 Solution
The solution will proceed as follows.
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 $Y$.
We now begin the solution.
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 $W$.
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 $W$.
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, $Z=\left( \frac{4}{7}, \frac{6}{7}, \frac{9}{7}\right)$.
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.
We begin with a lemma.
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.
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.
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 $\vec{z}=A\vec{x}$.
Then
1. $\vec{y} - A\vec{x} \in W^{\perp}$
2. $\|\vec{y} - A\vec{x}\| < \|\vec{y} - \vec{u}\|$ for all $\vec{u} \neq \vec{z} \in W$
We note a simple but useful observation.
引理 1:转置与点积 Transpose and Dot Product
Let $A$ be an $m\times n$ matrix. Then $A\vec{x} \cdot \vec{y} = \vec{x}\cdot A^T\vec{y}$
证明
Proof. This follows from the definitions: $A\vec{x} \cdot \vec{y}=\sum_{i,j}a_{ij}x_{j} y_{i} =\sum_{i,j}x_{j} a_{ji} y_{i}= \vec{x} \cdot A^T\vec{y}$
The next corollary gives the technique of least squares.
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.
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.
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.
Consider the following example.
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 $\left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 2 \\ 1 \\ 1 \end{array} \right]$
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 $\left[ \begin{array}{rr|r} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right]$
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 $\left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} \frac{5}{34} \\ \frac{7}{34} \end{array} \right]$
Consider another example.
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 $\left[ \begin{array}{rr} 2 & 1 \\ -1 & 3 \\ 4 & 5 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 3 \\ 2 \\ 9 \end{array} \right]$
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 $\left[ \begin{array}{rr|r} 1 & 0 & 1 \\ 0 & 1 & 1 \\ 0 & 0 & 0 \end{array} \right]$
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 $\left[ \begin{array}{rr} 21 & 19 \\ 19 & 35 \end{array} \right] \left[ \begin{array}{c} x \\ y \end{array} \right] =\left[ \begin{array}{c} 40 \\ 54 \end{array} \right]$
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.
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
$$\left[ \begin{array}{c} y_{1} \\ \vdots \\ y_{n} \end{array} \right] =\left[ \begin{array}{cc} x_{1} & 1 \\ \vdots & \vdots \\ x_{n} & 1 \end{array} \right] \left[ \begin{array}{c} m \\ b \end{array} \right]$$
which is of the form $\vec{y}=A\vec{x}$. It is desired to choose $m$ and $b$ to make
$$\left\| A\left[ \begin{array}{c} m \\ b \end{array} \right] -\left[ \begin{array}{c} y_{1} \\ \vdots \\ y_{n} \end{array} \right] \right\|^{2}$$
as small as possible. According to Theorem 5 and Corollary 1, the best values for $m$ and $b$ occur as the solution to
$$A^{T}A\left[ \begin{array}{c} m \\ b \end{array} \right] =A^{T}\left[ \begin{array}{c} y_{1} \\ \vdots \\ y_{n} \end{array} \right] ,\\ \mbox{where}\\ A=\left[ \begin{array}{cc} x_{1} & 1 \\ \vdots & \vdots \\ x_{n} & 1 \end{array} \right]$$
Thus, computing $A^{T}A,$
$$\left[ \begin{array}{cc} \sum_{i=1}^{n}x_{i}^{2} & \sum_{i=1}^{n}x_{i} \\ \sum_{i=1}^{n}x_{i} & n \end{array} \right] \left[ \begin{array}{c} m \\ b \end{array} \right] =\left[ \begin{array}{c} \sum_{i=1}^{n}x_{i}y_{i} \\ \sum_{i=1}^{n}y_{i} \end{array} \right]$$
Solving this system of equations for $m$ and $b$ (using Cramer's rule for example) yields:
$$m= \frac{-\left( \sum_{i=1}^{n}x_{i}\right) \left( \sum_{i=1}^{n}y_{i}\right) +\left( \sum_{i=1}^{n}x_{i}y_{i}\right) n}{\left( \sum_{i=1}^{n}x_{i}^{2}\right) n-\left( \sum_{i=1}^{n}x_{i}\right)^{2}} ,\quad b=\frac{-\left( \sum_{i=1}^{n}x_{i}\right) \sum_{i=1}^{n}x_{i}y_{i}+\left( \sum_{i=1}^{n}y_{i}\right) \sum_{i=1}^{n}x_{i}^{2}}{\left( \sum_{i=1}^{n}x_{i}^{2}\right) n-\left( \sum_{i=1}^{n}x_{i}\right)^{2}}$$
Consider the following example.
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: $\left\{ (0,1), (1,2), (2,2), (3,4), (4,5) \right\}$
解 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 $\begin{aligned} m &= \frac{- 10 \cdot 14 + 5 \cdot 38}{5\cdot 30-10^2} = 1.00 \\ b &= \frac{- 10 \cdot 38 + 14\cdot 30}{5\cdot 30-10^2} = 0.80 \end{aligned}$
The least squares regression line for the set of data points is: $\vec{y} = \vec{x}+.8$
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.
The following diagram shows the data points and the corresponding regression line.
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.
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.
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.
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.
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.
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.
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 $\vec{k}$.
Given a vector $\vec{u}=\left[ u_{1} \cdots u_{n}\right]^T ,$ it follows that $\vec{u}=u_{1}\vec{e}_{1}+\cdots +u_{n}\vec{e}_{n}= \sum_{k=1}^{n}u_{i}\vec{e}_{i}$
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. $\begin{aligned} \vec{u}+\vec{v}& =\left[ u_{1}+v_{1} \cdots u_{n}+v_{n}\right]^T \\ & =\sum_{i=1}^{n}\left( u_{i}+v_{i}\right) \vec{e}_{i}\end{aligned}$
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.
Consider now some examples of vector addition.
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 $\vec{i}$.
解 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.
Consider another example.
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.
解 Solution
A picture of this situation follows.
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 $\vec{u} = \frac{100}{\sqrt{2}} \vec{i}+ \frac{100}{\sqrt{2 }}\vec{j} = \left[ \begin{array}{rr} \frac{100}{\sqrt{2}} & \frac{100}{\sqrt{2}} \end{array} \right]^T$
This example also motivates the concept of velocity, defined below.
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.
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.
Consider the following example.
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.
解 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.
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 $\left[ \begin{array}{rrr} 1 & 2 & 1 \end{array} \right]^T + \left[ \begin{array}{rrr} \frac{5}{3} & \frac{1}{60} & \frac{1}{60} \end{array} \right]^T =\left[ \begin{array}{rrr} \frac{8}{3} & \frac{121}{60} & \frac{121}{60} \end{array} \right]^T$
Now consider an example which involves combining two velocities.
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?
解 Solution
Consider the following picture which demonstrates the above scenario.
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.
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.
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. $d = \sqrt{ \left(\frac{2}{3} \right)^2 + \left( \frac{1}{2} \right) ^2 } = \frac{5}{6} \mbox{ km}$
Therefore, the swimmer travels a total distance of $\frac{5}{6}$ kilometers.
功 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.
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.
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, $\vec{F} = \vec{F}_{\|} + \vec{F}_{\bot}$
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 $\|\vec{F}\| \|\vec{PQ}\| \cos \theta = \|\vec{F}\| \|\vec{q}-\vec{p}\| \cos \theta$
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.
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 $\vec{F}\bullet \left( \vec{q}-\vec{p}\right) .$
Consider the following example.
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.
解 Solution
First, compute the vector $\vec{q} - \vec{p}$, given by $\left[ \begin{array}{rrr} -9 & -3 & 4 \end{array} \right]^T - \left[ \begin{array}{rrr} 1 & 2 & 3 \end{array} \right]^T = \left[ \begin{array}{rrr} -10 & -5 & 1 \end{array} \right]^T$
According to Definition 3 the work done is $\begin{aligned} \left[ \begin{array}{rrr} 2 & 7 & 3 \end{array} \right]^T \bullet \left[ \begin{array}{rrr} -10 & -5 & 1 \end{array} \right]^T & =-20+\left( -35\right) +\left( -3\right) \\ & =-58 \text{ Newton meters}\end{aligned}$
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.
习题 Exercises
习题 1 Exercise 1
Find $-3\left[\begin{array}{c}5\\-1\\2\\-3\end{array}\right]+5\left[\begin{array}{c}-8\\2\\-3\\6\end{array}\right]$.
答案 Answer:$\left[\begin{array}{c}-55\\13\\-21\\39\end{array}\right]$
习题 2 Exercise 2
Find $-7\left[\begin{array}{c}6\\0\\4\\-1\end{array}\right]+6\left[\begin{array}{c}-13\\-1\\1\\6\end{array}\right]$.
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 $\vec{u}_{1}=\left[\begin{array}{c}3\\1\\-1\end{array}\right]\quad\text{and}\quad\vec{u}_{2}=\left[\begin{array}{c}2\\-2\\1\end{array}\right].$
答案 Answer:$\left[\begin{array}{c}4\\4\\-3\end{array}\right]=2\left[\begin{array}{c}3\\1\\-1\end{array}\right]-\left[\begin{array}{c}2\\-2\\1\end{array}\right]$
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 $\vec{u}_{1}=\left[\begin{array}{c}3\\1\\-1\end{array}\right]\quad\text{and}\quad\vec{u}_{2}=\left[\begin{array}{c}2\\-2\\1\end{array}\right].$
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.
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.
习题 6 Exercise 6
Find parametric equations for the line through the point $(7, 7, 1)$ with a direction vector $\vec{d}=\left[\begin{array}{c}1\\6\\2\end{array}\right]$.
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.
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.
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.
习题 10 Exercise 10
Find parametric equations for the line through $(6, 5,-2)$ and $(5, 1, 2)$.
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 $\vec{d}=\left[\begin{array}{c}1\\1\\3\end{array}\right]$.
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.
习题 13 Exercise 13
Find the vector equation and parametric equations for the line through the two points $(4, 10, 0),$ $(1,-5,-6)$.
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 $Q$.
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.
习题 16 Exercise 16
Find $\left[\begin{array}{c}1\\2\\3\\4\end{array}\right]\bullet\left[\begin{array}{c}2\\0\\1\\3\end{array}\right]$.
答案 Answer:$\left[\begin{array}{c}1\\2\\3\\4\end{array}\right]\bullet\left[\begin{array}{c}2\\0\\1\\3\end{array}\right]=17$
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.
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.
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 $|\vec{u}\ast\vec{v}|\leq (\vec{u}\ast\vec{u})^{1/2}(\vec{v}\ast\vec{v})^{1/2}$
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.
习题 19 Exercise 19
Let $\vec{a}$, $\vec{b}$ be vectors. Show that $\left(\vec{a}\bullet\vec{b}\right)=\frac{1}{4}\left(\|\vec{a}+\vec{b}\|^2-\|\vec{a}-\vec{b}\|^2\right).$
习题 20 Exercise 20
Using the axioms of the dot product, prove the parallelogram identity: $\|\vec{a}+\vec{b}\|^2+\|\vec{a}-\vec{b}\|^2=2\|\vec{a}\|^2+2\|\vec{b}\|^2$
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.
答案 Answer:$A\vec{x}\bullet\vec{y}=\sum_k(A\vec{x})_ky_k=\sum_k\sum_iA_{ki}x_iy_k=\sum_i\sum_kA^T_{ik}x_iy_k=\vec{x}\bullet A^T\vec{y}$
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.
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 $B^TA^T-(AB)^T=0$.
习题 23 Exercise 23
Find the angle between the vectors $\vec{u}=\left[\begin{array}{r}3\\-1\\-1\end{array}\right],$ $\vec{v}=\left[\begin{array}{c}1\\4\\2\end{array}\right]$
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.
习题 24 Exercise 24
Find the angle between the vectors $\vec{u}=\left[\begin{array}{r}1\\-2\\1\end{array}\right],$ $\vec{v}=\left[\begin{array}{r}1\\2\\-7\end{array}\right]$
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.
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 $\vec{v}=\left[\begin{array}{c}1\\2\\3\end{array}\right]$.
答案 Answer:$\frac{\vec{u}\bullet\vec{v}}{\vec{u}\bullet\vec{u}}\vec{u}=\frac{-5}{14}\left[\begin{array}{c}1\\2\\3\end{array}\right]=\left[\begin{array}{r}-\frac{5}{14}\\-\frac{5}{7}\\-\frac{15}{14}\end{array}\right]$
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 $\vec{v}=\left[\begin{array}{c}1\\0\\3\end{array}\right]$.
答案 Answer:$\frac{\vec{u}\bullet\vec{v}}{\vec{u}\bullet\vec{u}}\vec{u}=\frac{-5}{10}\left[\begin{array}{c}1\\0\\3\end{array}\right]=\left[\begin{array}{r}-\frac{1}{2}\\0\\-\frac{3}{2}\end{array}\right]$
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 $\vec{v}=\left[\begin{array}{c}1\\2\\3\\0\end{array}\right]$.
答案 Answer:$\frac{\vec{u}\bullet\vec{v}}{\vec{u}\bullet\vec{u}}\vec{u}=\frac{\left[\begin{array}{cccc}1&2&-2&1\end{array}\right]^T\bullet\left[\begin{array}{cccc}1&2&3&0\end{array}\right]^T}{1+4+9}\left[\begin{array}{c}1\\2\\3\\0\end{array}\right]=\left[\begin{array}{r}-\frac{1}{14}\\-\frac{1}{7}\\-\frac{3}{14}\\0\end{array}\right]$
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 $P$.
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 $P$.
习题 30 Exercise 30
Does it make sense to speak of $\text{proj}_{\vec{0}} (\vec{w})$?
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.
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 $\vec{w}= \text{proj}_{\vec{v}}\vec{w}$?
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 $\vec{v}$.
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 $\vec{w}_{\perp} =\vec{w}-\text{proj}_{\vec{v}}(\vec{w})$.
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 $\begin{aligned}\text{proj}_{\vec{v}}(\vec{w})+\text{proj}_{\vec{v}}(\vec{u})&=\frac{\vec{u}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v}+\frac{\vec{w}\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v} \\ &=\frac{(\vec{u}+\vec{w})\bullet\vec{v}}{\|\vec{v}\|^2}\vec{v} \\ &=\text{proj}_{\vec{v}}(\vec{w}+\vec{u})\end{aligned}$
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.
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 $\vec{u}$.
习题 34 Exercise 34
Show that if $\vec{a}\times\vec{u}=\vec{0}$ for any unit vector $\vec{u}$, then $\vec{a}=\vec{0}$.
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.
习题 35 Exercise 35
Find the area of the triangle determined by the three points $(1, 2, 3),$ $(4, 2, 0)$ and $(-3, 2, 1)$.
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 $9$.
习题 36 Exercise 36
Find the area of the triangle determined by the three points $(1, 0, 3),$ $(4, 1, 0)$ and $(-3, 1, 1)$.
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 $\frac{1}{2}\sqrt{1+(18)^2+49}=\frac{1}{2}\sqrt{374}$
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?
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.
习题 38 Exercise 38
Find the area of the parallelogram determined by the vectors $\left[\begin{array}{c}1\\2\\3\end{array}\right]$, $\left[\begin{array}{r}3\\-2\\1\end{array}\right]$.
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 $8\sqrt{3}$.
习题 39 Exercise 39
Find the area of the parallelogram determined by the vectors $\left[\begin{array}{c}1\\0\\3\end{array}\right]$, $\left[\begin{array}{r}4\\-2\\1\end{array}\right]$.
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 $\sqrt{36+121+4}=\sqrt{161}$.
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 $\left(\vec{i}\times\vec{j}\right)\times\vec{k}$.
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.
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.
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. $\left\|\begin{array}{ccc}\vec{i}&\vec{j}&\vec{k}\\1&0&0\\0&1&0\end{array}\right\|=\vec{k}$
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 $\omega_1\vec{i}+\omega_2\vec{j}+\omega_3\vec{k}$.
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 $\left[\begin{array}{c}3\\2\\3\end{array}\right]$.
答案 Answer:$\left\|\begin{array}{ccc}1&-7&-5 \\ 1&-2&-6 \\ 3&2&3\end{array}\right\|=113$
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?
答案 Answer:Yes. It will involve the sum of product of integers and so it will be an integer.
习题 45 Exercise 45
What does it mean geometrically if the box product of three vectors gives zero?
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.
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 $0$.
答案 Answer:$\vec{x}\bullet\left(\vec{a}\times\vec{b}\right)=0$
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.
习题 48 Exercise 48
Simplify $(\vec{u}\times\vec{v})\bullet [(\vec{v}\times\vec{w})\times (\vec{w}\times\vec{z})]$.
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 $(\vec{u}\times\vec{v})\bullet [\vec{v},\vec{w},\vec{z}]\vec{w}=[\vec{v},\vec{w},\vec{z}][\vec{u},\vec{v},\vec{w}]$
习题 49 Exercise 49
Simplify $\|\vec{u}\times\vec{v}\|^2+(\vec{u}\bullet\vec{v})^2-\|\vec{u}\|^2\|\vec{v}\|^2$.
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 $0$.
习题 50 Exercise 50
For $\vec{u},$ $\vec{v},$ $\vec{w}$ functions of $t$, prove the following product rules: $\begin{aligned}(\vec{u}\times\vec{v})'&=\vec{u}'\times\vec{v}+\vec{u}\times\vec{v}' \\ (\vec{u}\bullet\vec{v})'&=\vec{u}'\bullet\vec{v}+\vec{u}\bullet\vec{v}'\end{aligned}$
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 $(\vec{u}\times\vec{v})'=\vec{u}'\times\vec{v}+\vec{u}\times\vec{v}'$.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 $\mathrm{span}\{\vec{x}_{1},\cdots ,\vec{x}_{k}\}$.
答案 Answer:$\sum\limits_{i=1}^k 0\vec{x}_{k}=\vec{0}$
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. $\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right],$ $\left[\begin{array}{r}1\\10\\2\\1\end{array}\right]$
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. $\left[\begin{array}{r}-1\\-2\\2\\3\end{array}\right],$ $\left[\begin{array}{r}-3\\-4\\3\\3\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\4\\3\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\6\\4\end{array}\right]$
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. $\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\6\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}-1\\-4\\1\\-1\end{array}\right],$ $\left[\begin{array}{r}1\\6\\-2\\1\end{array}\right]$
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. $\left[\begin{array}{r}1\\-1\\3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\6\\34\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\7\\1\end{array}\right],$ $\left[\begin{array}{r}1\\0\\8\\1\end{array}\right]$
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. $\left[\begin{array}{r}1\\3\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-1\\1\end{array}\right],$ $\left[\begin{array}{r}-3\\-10\\3\\-3\end{array}\right],$ $\left[\begin{array}{r}1\\4\\0\\1\end{array}\right]$
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. $\left[\begin{array}{r}1\\3\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-5\\1\end{array}\right],$ $\left[\begin{array}{r}1\\4\\-4\\1\end{array}\right],$ $\left[\begin{array}{r}1\\10\\-14\\1\end{array}\right]$
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. $\left[\begin{array}{r}1\\0\\3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\1\\8\\1\end{array}\right],$ $\left[\begin{array}{r}1\\7\\34\\1\end{array}\right],$ $\left[\begin{array}{r}1\\1\\7\\1\end{array}\right]$
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. $\left[\begin{array}{r}1\\4\\-2\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-3\\1\end{array}\right],$ $\left[\begin{array}{r}1\\7\\-5\\1\end{array}\right],$ $\left[\begin{array}{r}1\\5\\-2\\1\end{array}\right]$
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. $\left[\begin{array}{r}1\\2\\2\\-4\end{array}\right],$ $\left[\begin{array}{r}3\\4\\1\\-4\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\0\\4\end{array}\right],$ $\left[\begin{array}{r}0\\-1\\-2\\5\end{array}\right]$
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. $\left[\begin{array}{r}2\\3\\1\\-3\end{array}\right],$ $\left[\begin{array}{r}-5\\-6\\0\\3\end{array}\right],$ $\left[\begin{array}{r}-1\\-2\\1\\3\end{array}\right],$ $\left[\begin{array}{r}-1\\-2\\0\\4\end{array}\right]$
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 $\vec{u}\in M$.
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.
答案 Answer:No. $\left[\begin{array}{c}1\\0\\0\\0\end{array}\right]\in M$ but $10\left[\begin{array}{c}1\\0\\0\\0\end{array}\right]\notin M$.
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.
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.
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.
答案 Answer:This is a subspace because it is closed with respect to vector addition and scalar multiplication.
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.
答案 Answer:Yes, this is a subspace because it is closed with respect to vector addition and scalar multiplication.
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.
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.
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.
答案 Answer:This is a subspace. It is closed with respect to vector addition and scalar multiplication.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
习题 106 Exercise 106
If you have $6$ vectors in $\mathbb{R}^{5}$, is it possible they are linearly independent? Explain.
答案 Answer:They can't be.
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.
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 $c_{i}=0$.
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.
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.
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.
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.
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 $p+q-n\leq k$.
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 $\text{ker}(AB)\subseteq\text{span}\left\{\vec{u}_{1},\cdots,\vec{u}_{r},\vec{z}_{1},\cdots,\vec{z}_{k}\right\}$.
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, $\begin{aligned}\text{dim}(\text{ker}(AB))&\leq k+r=\text{dim}(B(\mathbb{R}^{p})\cap\text{ker}(A))+\text{dim}(\text{ker}(B))\\ &\leq\text{dim}(\text{ker}(A))+\text{dim}(\text{ker}(B))\end{aligned}$
习题 112 Exercise 112
Show that if $A$ is an $m\times n$ matrix, then $\text{ker}(A)$ is a subspace of $\mathbb{R}^{n}$.
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.
习题 113 Exercise 113
Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrrrr}1&3&0&-2&0&3\\3&9&1&-7&0&8\\1&3&1&-3&1&-1\\1&3&-1&-1&-2&10\end{array}\right]$
习题 114 Exercise 114
Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrrrr}1&3&0&-2&7&3\\3&9&1&-7&23&8\\1&3&1&-3&9&2\\1&3&-1&-1&5&4\end{array}\right]$
习题 115 Exercise 115
Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrrrr}1&0&3&0&7&0\\3&1&10&0&23&0\\1&1&4&1&7&0\\1&-1&2&-2&9&1\end{array}\right]$
习题 116 Exercise 116
Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrr}1&0&3\\3&1&10\\1&1&4\\1&-1&2\end{array}\right]$
习题 117 Exercise 117
Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrrr}0&0&-1&0&1\\1&2&3&-2&-18\\1&2&2&-1&-11\\-1&-2&-2&1&11\end{array}\right]$
习题 118 Exercise 118
Find the rank of the following matrix. Also find a basis for the row and column spaces. $\left[\begin{array}{rrrr}1&0&3&0\\3&1&10&0\\-1&1&-2&1\\1&-1&2&-2\end{array}\right]$
习题 119 Exercise 119
Find $\text{ker}(A)$ for the following matrices.
1. $A=\left[\begin{array}{rr}2&3\\4&6\end{array}\right]$
2. $A=\left[\begin{array}{rrr}1&0&-1\\-1&1&3\\3&2&1\end{array}\right]$
3. $A=\left[\begin{array}{rrr}2&4&0\\3&6&-2\\1&2&-2\end{array}\right]$
4. $A=\left[\begin{array}{rrrr}2&-1&3&5\\2&0&1&2\\6&4&-5&-6\\0&2&-4&-6\end{array}\right]$
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.
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.
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.
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.
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.
习题 125 Exercise 125
Here are some matrices. Label according to whether they are symmetric, skew symmetric, or orthogonal.
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. $\left[\begin{array}{ccc}1&2&-3\\2&1&4\\-3&4&7\end{array}\right]$
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
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.
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.
习题 127 Exercise 127
Suppose $U$ is an orthogonal $n\times n$ matrix. Explain why $\text{rank}(U)=n$.
答案 Answer:You could observe that $\det(UU^{T})=(\det(U))^{2}-1$ so $\det(U)\neq 0$.
习题 128 Exercise 128
Fill in the missing entries to make the matrix orthogonal. $\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\underline{\hspace{1.2em}}&\underline{\hspace{1.2em}}\\\underline{\hspace{1.2em}}&\frac{\sqrt{6}}{3}&\underline{\hspace{1.2em}}\end{array}\right].$
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 $$a=1/\sqrt{3},\quad b=1/\sqrt{3}.$$ $$\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&1/\sqrt{3}\\0&\frac{\sqrt{6}}{3}&1/\sqrt{3}\end{array}\right]\left[\begin{array}{ccc}\frac{-1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&\frac{1}{\sqrt{3}}\\\frac{1}{\sqrt{2}}&\frac{-1}{\sqrt{6}}&1/\sqrt{3}\\0&\frac{\sqrt{6}}{3}&1/\sqrt{3}\end{array}\right]^{T}=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&1\end{array}\right]$$
习题 129 Exercise 129
Fill in the missing entries to make the matrix orthogonal. $\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\underline{\hspace{1.2em}}&\underline{\hspace{1.2em}}\\\underline{\hspace{1.2em}}&0&\underline{\hspace{1.2em}}\end{array}\right]$
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 $$a=\frac{1}{3\sqrt{2}},\quad b=\frac{4}{3\sqrt{2}}.$$ $$\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&\frac{1}{3\sqrt{2}}\\-\frac{1}{3}&0&\frac{4}{3\sqrt{2}}\end{array}\right]\left[\begin{array}{ccc}\frac{2}{3}&\frac{\sqrt{2}}{2}&\frac{1}{6}\sqrt{2}\\\frac{2}{3}&\frac{-\sqrt{2}}{2}&\frac{1}{3\sqrt{2}}\\-\frac{1}{3}&0&\frac{4}{3\sqrt{2}}\end{array}\right]^{T}=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&1\end{array}\right]$$
习题 130 Exercise 130
Fill in the missing entries to make the matrix orthogonal. $\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&\underline{\hspace{1.2em}}\\\frac{2}{3}&0&\underline{\hspace{1.2em}}\\\underline{\hspace{1.2em}}&\underline{\hspace{1.2em}}&\frac{4}{15}\sqrt{5}\end{array}\right]$
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 $c=\frac{2}{3\sqrt{5}}$, $d=\frac{-5}{3\sqrt{5}}$. $$\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&\frac{2}{3\sqrt{5}}\\\frac{2}{3}&0&\frac{-5}{3\sqrt{5}}\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]\left[\begin{array}{ccc}\frac{1}{3}&-\frac{2}{\sqrt{5}}&\frac{2}{3\sqrt{5}}\\\frac{2}{3}&0&\frac{-5}{3\sqrt{5}}\\\frac{2}{3}&\frac{1}{\sqrt{5}}&\frac{4}{15}\sqrt{5}\end{array}\right]^{T}=\left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&1\end{array}\right]$$
习题 131 Exercise 131
Find an orthonormal basis for the span of each of the following sets of vectors.
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. $\left[\begin{array}{r}3\\0\\-4\end{array}\right],\ \left[\begin{array}{r}11\\0\\2\end{array}\right],\ \left[\begin{array}{r}1\\1\\7\end{array}\right]$
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. $\left[\begin{array}{c}\frac{3}{5}\\0\\-\frac{4}{5}\end{array}\right],\ \left[\begin{array}{c}\frac{4}{5}\\0\\\frac{3}{5}\end{array}\right],\ \left[\begin{array}{c}0\\1\\0\end{array}\right]$
习题 132 Exercise 132
Using the Gram Schmidt process find an orthonormal basis for the following span: $\mathrm{span}\left\{\left[\begin{array}{r}1\\2\\1\end{array}\right],\ \left[\begin{array}{r}2\\-1\\3\end{array}\right],\ \left[\begin{array}{r}1\\0\\0\end{array}\right]\right\}$
答案 Answer:A solution is $\left[\begin{array}{c}\frac{1}{6}\sqrt{6}\\\frac{1}{3}\sqrt{6}\\\frac{1}{6}\sqrt{6}\end{array}\right],\ \left[\begin{array}{c}\frac{3}{10}\sqrt{2}\\-\frac{2}{5}\sqrt{2}\\\frac{1}{2}\sqrt{2}\end{array}\right],\ \left[\begin{array}{c}\frac{7}{15}\sqrt{3}\\-\frac{1}{15}\sqrt{3}\\-\frac{1}{3}\sqrt{3}\end{array}\right]$
习题 133 Exercise 133
Using the Gram Schmidt process find an orthonormal basis for the following span: $\mathrm{span}\left\{\left[\begin{array}{r}1\\2\\1\\0\end{array}\right],\ \left[\begin{array}{r}2\\-1\\3\\1\end{array}\right],\ \left[\begin{array}{r}1\\0\\0\\1\end{array}\right]\right\}$
答案 Answer:Then a solution is $\left[\begin{array}{c}\frac{1}{6}\sqrt{6}\\\frac{1}{3}\sqrt{6}\\\frac{1}{6}\sqrt{6}\\0\end{array}\right],\ \left[\begin{array}{c}\frac{1}{6}\sqrt{2}\sqrt{3}\\-\frac{2}{9}\sqrt{2}\sqrt{3}\\\frac{5}{18}\sqrt{2}\sqrt{3}\\\frac{1}{9}\sqrt{2}\sqrt{3}\end{array}\right],\ \left[\begin{array}{c}\frac{5}{111}\sqrt{3}\sqrt{37}\\\frac{1}{133}\sqrt{3}\sqrt{37}\\-\frac{17}{333}\sqrt{3}\sqrt{37}\\\frac{22}{333}\sqrt{3}\sqrt{37}\end{array}\right]$
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.
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 $\left[\begin{array}{c}\frac{1}{5}\sqrt{5}\\0\\\frac{2}{5}\sqrt{5}\end{array}\right],\ \left[\begin{array}{c}-\frac{3}{35}\sqrt{5}\sqrt{14}\\\frac{1}{14}\sqrt{5}\sqrt{14}\\\frac{3}{70}\sqrt{5}\sqrt{14}\end{array}\right]$
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 $(3,4,1)$.
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 $(3,4,1)$.
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.
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.
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 $t$.
习题 140 Exercise 140
Find the least squares solution to the following system. $\begin{aligned}x+2y&=1\\2x+3y&=2\\3x+5y&=4\end{aligned}$
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: $$\left[\begin{array}{c}\frac{2}{3}\\\frac{1}{3}\end{array}\right]$$
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.
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.
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.
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.
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?
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.
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 $\left[\begin{array}{r}7\\1\\3\end{array}\right].$
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.
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?
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 $\left(50+\frac{300}{\sqrt{2}}\right)\vec{i}+\frac{300}{\sqrt{2}}\vec{j}$
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?
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?
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.
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 $\left(40,150\right)+150\left[\begin{array}{cc}\frac{1}{2}&\frac{\sqrt{3}}{2}\end{array}\right]+\left[\begin{array}{cc}40&0\end{array}\right]=\left[\begin{array}{cc}155&75\sqrt{3}+150\end{array}\right]=\left[\begin{array}{cc}155.0&279.9\end{array}\right]$
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?
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.
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?
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.
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?
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 $\left[\begin{array}{cc}\frac{2}{3}&\frac{\sqrt{5}}{3}\end{array}\right].$
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.
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.
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?
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.
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, $\vec{D}$.
答案 Answer:$$\text{proj}_{\vec{D}}\left(\vec{F}\right)=\frac{\vec{F}\bullet\vec{D}}{\|\vec{D}\|}\frac{\vec{D}}{\|\vec{D}\|}=\left(\|\vec{F}\|\cos\theta\right)\frac{\vec{D}}{\|\vec{D}\|}=\left(\|\vec{F}\|\cos\theta\right)\vec{u}$$
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?
答案 Answer:$$40\cos\left(\frac{20}{180}\pi\right)100=3758.8$$
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?
答案 Answer:$$20\cos\left(\frac{\pi}{6}\right)200=3464.1$$
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?
答案 Answer:$$20\left(\cos\frac{\pi}{4}\right)300=4242.6$$
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.
答案 Answer:$$200\left(\cos\left(\frac{\pi}{6}\right)\right)20=3464.1$$
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?
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.
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?
答案 Answer:$$\begin{aligned}\vec{F}_{1}\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10+\vec{F}_{2}\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10&=\left(\vec{F}_{1}+\vec{F}_{2}\right)\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10\\ &=\left[\begin{array}{r}6\\4\\-4\end{array}\right]\bullet\left[\begin{array}{r}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\\0\end{array}\right]10\\ &=50\sqrt{2}\end{aligned}$$
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.
答案 Answer:$$\left[\begin{array}{r}2\\3\\-4\end{array}\right]\bullet\left[\begin{array}{r}0\\\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\end{array}\right]20=-10\sqrt{2}$$