User:唐舞麟/沙盒
线性代数（来源于英文维基教科书）[编辑]
The book was designed specifically for students who had not previously been exposed to mathematics as mathematicians view it. That is, as a subject whose goal is to rigorously prove theorems starting from clear consistent definitions. This book attempts to build students up from a background where mathematics is simply a tool that provides useful calculations to the point where the students have a grasp of the clear and precise nature of mathematics. A more detailed discussion of the prerequisites and goal of this book is given in the introduction.
这本书是专门为那些以前没有接触过数学的学生设计的，因为他们是数学家。也就是说，作为一个目标是从清晰一致的定义开始严格证明定理的学科。这本书试图建立学生从一个背景，数学只是一个工具，提供有用的计算点，学生有一个清晰和精确的数学性质的掌握。引言中对本书的先决条件和目标进行了更详细的讨论。
Table of Contents[编辑]
Linear Systems[编辑]
 Solving Linear Systems（Jul 13, 2009）
 Linear Geometry of nSpace （Jul 13, 2009）
 Reduced Echelon Form （Jul 13, 2009）
 Topic: Computer Algebra Systems （Jul 13, 2009）
 Topic: InputOutput Analysis （Jul 13, 2009）
 InputOutput Analysis M File （Mar 24 2008）
 Topic: Accuracy of Computations （Jul 13, 2009）
 Topic: Analyzing Networks （Jul 13, 2009）
 Topic: Speed of Gauss' Method （Mar 24, 2008）
Vector Spaces （Apr 17, 2009）[编辑]
 Definition of Vector Space（Apr 17, 2009）
 Linear Independence（Apr 17, 2009）
 Basis and Dimension（Apr 17, 2009）
 Topic: Fields（Apr 17, 2009）
 Topic: Crystals（Apr 17, 2009）
 Topic: Voting Paradoxes（Apr 17, 2009）
 Topic: Dimensional Analysis（Apr 17, 2009）
Maps Between Spaces[编辑]
 Isomorphisms（Jun 21, 2009）
 Homomorphisms（Jun 21, 2009）
 Computing Linear Maps（Jun 21, 2009）
 Matrix Operations（Jun 21, 2009）
 Change of Basis（Jun 21, 2009）
 Projection（Jun 21, 2009）
 Topic: Line of Best Fit（Jun 21, 2009）
 Topic: Geometry of Linear Maps（Jun 21, 2009）
 Topic: Markov Chains（Jun 21, 2009）
 Topic: Orthonormal Matrices（Jun 21, 2009）
Determinants（Jun 21, 2009）[编辑]
 Definition（Jun 21, 2009）
 Geometry of Determinants（Jun 21, 2009）
 Other Formulas for Determinants（Jun 21, 2009）
 Topic: Cramer's Rule（Jun 21, 2009）
 Topic: Speed of Calculating Determinants（Jun 21, 2009）
 Topic: Projective Geometry（Jun 21, 2009）
Similarity（Jun 24, 2009）[编辑]
 Complex Vector Spaces（Jun 24, 2009）
 Similarity
 Nilpotence（Jun 24, 2009）
 Jordan Form（Jun 24, 2009）
 Topic: Geometry of Eigenvalues（Jun 24, 2009）
 Topic: The Method of Powers（Jun 24, 2009）
 Topic: Stable Populations（Jun 24, 2009）
 Topic: Linear Recurrences（Jun 24, 2009）
Unitary Transformations[编辑]
Appendix[编辑]
封面[编辑]
符号说明[编辑]
, ,  real numbers, reals greater than , ordered tuples of reals 
 natural numbers: 

complex numbers 

set of . . . such that . . . 
,  interval (open or closed) of reals between and 

sequence; like a set but order matters 

vector spaces 

vectors 
,  zero vector, zero vector of 

bases 
 standard basis for 

basis vectors 

matrix representing the vector 
 set of th degree polynomials 
 set of matrices 
 span of the set 

direct sum of subspaces 

isomorphic spaces 

homomorphisms, linear maps 

matrices 

transformations; maps from a space to itself 

square matrices 
 matrix representing the map 
 matrix entry from row , column 
 determinant of the matrix
矩阵的行列式 
 rangespace and nullspace of the map 

generalized rangespace and nullspace 
Lower case Greek alphabet
小写希腊字母[编辑]
About the Cover. This is Cramer's Rule for the system , . The size of the first box is the determinant shown (the absolute value of the size is the area). The size of the second box is times that, and equals the size of the final box. Hence, is the final determinant divided by the first determinant.
介绍[编辑]
This book helps students to master the material of a standard undergraduate linear algebra course.
这本书帮助学生掌握标准的本科线性代数课程的材料。
The material is standard in that the topics covered are Gaussian reduction, vector spaces, linear maps, determinants, and eigenvalues and eigenvectors. The audience is also standard: sophomores or juniors, usually with a background of at least one semester of calculus and perhaps with as much as three semesters.
材料是标准的，因为涵盖的主题是高斯约化，向量空间，线性映射，行列式，特征值和特征向量。听众也是标准的：大二或大三，通常至少有一个学期的微积分背景，也许有三个学期的时间。
The help that it gives to students comes from taking a developmental approach—this book's presentation emphasizes motivation and naturalness, driven home by a wide variety of examples and extensive, careful, exercises. The developmental approach is what sets this book apart, so some expansion of the term is appropriate here.
它给学生的帮助来自于采取一种发展的方法这本书的介绍强调动机和自然性，由各种各样的例子和广泛的，仔细的，练习。发展的方法使这本书与众不同，所以在这里对这个术语进行一些扩展是合适的。
Courses in the beginning of most mathematics programs reward students less for understanding the theory and more for correctly applying formulas and algorithms. Later courses ask for mathematical maturity: the ability to follow different types of arguments, a familiarity with the themes that underlay many mathematical investigations like elementary set and function facts, and a capacity for some independent reading and thinking. Linear algebra is an ideal spot to work on the transition between the two kinds of courses. It comes early in a program so that progress made here pays off later, but also comes late enough that students are often majors and minors. The material is coherent, accessible, and elegant. There are a variety of argument styles—proofs by contradiction, if and only if statements, and proofs by induction, for instance—and examples are plentiful.
大多数数学课程开始时的课程对学生的奖励较少，因为他们理解了理论，而更多的是因为他们正确地应用了公式和算法。以后的课程要求数学成熟：能够理解不同类型的论据，熟悉许多数学研究的主题，如基本集合和函数事实，以及独立阅读和思考的能力。线性代数是研究这两种课程之间过渡的理想场所。它在一个项目中出现得早，这样在这里取得的进步会得到回报，但也会来得太晚，以至于学生通常都是主修生和未成年学生。材料连贯，通俗易懂，优雅大方。用矛盾证明、当且仅当语句证明、归纳证明等多种论证方式，如实例和实例丰富。
So, the aim of this book's exposition is to help students develop from being successful at their present level, in classes where a majority of the members are interested mainly in applications in science or engineering, to being successful at the next level, that of serious students of the subject of mathematics itself.
因此，这本书的目的是帮助学生从目前的水平，在大多数成员主要对科学或工程应用感兴趣的课程中取得成功，发展到下一阶段的成功，即数学学科本身的严肃学生。
Helping students make this transition means taking the mathematics seriously, so all of the results in this book are proved. On the other hand, we cannot assume that students have already arrived, and so in contrast with more abstract texts, we give many examples and they are often quite detailed.
帮助学生完成这一转变意味着要认真对待数学，因此本书中的所有结果都得到了证明。另一方面，我们不能假设学生已经到了，因此与更抽象的文本相比，我们给出了许多例子，而且往往非常详细。
In the past, linear algebra texts commonly made this transition abruptly. They began with extensive computations of linear systems, matrix multiplications, and determinants. When the concepts—vector spaces and linear maps—finally appeared, and definitions and proofs started, often the change brought students to a stop. In this book, while we start with a computational topic, linear reduction, from the first we do more than compute. We do linear systems quickly but completely, including the proofs needed to justify what we are computing. Then, with the linear systems work as motivation and at a point where the study of linear combinations seems natural, the second chapter starts with the definition of a real vector space. This occurs by the end of the third week.
在过去，线性代数文本通常会突然进行这种转换。他们开始广泛计算线性系统，矩阵乘法和行列式。当概念向量空间和线性映射最终出现，定义和证明开始时，这种变化常常使学生停止。在这本书中，我们从一个计算主题开始，线性化简，从一开始我们做的不仅仅是计算。我们快速但完整地处理线性系统，包括证明我们正在计算的东西。然后，以线性系统为动力，在研究线性组合似乎很自然的地方，第二章从实向量空间的定义开始。这将在第三周结束时发生。
Another example of our emphasis on motivation and naturalness is that the third chapter on linear maps does not begin with the definition of homomorphism, but with that of isomorphism. That's because this definition is easily motivated by the observation that some spaces are "just like" others. After that, the next section takes the reasonable step of defining homomorphism by isolating the operationpreservation idea. This approach loses mathematical slickness, but it is a good trade because it comes in return for a large gain in sensibility to students.
我们强调动机和自然性的另一个例子是，关于线性映射的第三章没有从同态的定义开始，而是从同构的定义开始。这是因为这个定义很容易被一些空间“和”其他空间“一样”的观察所激发。然后，下一节通过隔离操作保持的思想，采取合理的步骤来定义同态。这种方法失去了数学上的圆滑，但它是一种很好的交易，因为它可以让学生在情感上得到很大的提高。
One aim of a developmental approach is that students should feel throughout the presentation that they can see how the ideas arise, and perhaps picture themselves doing the same type of work.
发展性教学法的一个目的是让学生在整个演示过程中感觉到他们可以看到想法是如何产生的，也许还能想象自己在做同样类型的工作。
The clearest example of the developmental approach taken here—and the feature that most recommends this book—is the exercises. A student progresses most while doing the exercises, so they have been selected with great care. Each problem set ranges from simple checks to reasonably involved proofs. Since an instructor usually assigns about a dozen exercises after each lecture, each section ends with about twice that many, thereby providing a selection. There are even a few problems that are challenging puzzles taken from various journals, competitions, or problems collections. (These are marked with a "?" and as part of the fun, the original wording has been retained as much as possible.) In total, the exercises are aimed to both build an ability at, and help students experience the pleasure of, doing mathematics.
最清楚的例子，在这里采取的发展方法和特点，最推荐这本书是练习。学生在做练习时进步最大，所以他们是经过精心挑选的。每个习题集的范围从简单的检查到合理涉及的证明。由于教师通常在每堂课后布置十几个练习题，所以每节课结束时的练习数是原来的两倍，因此提供了一个选择题。甚至有一些问题是挑战性的难题从各种杂志，比赛，或问题收集。（这些标记有“？”总的来说，这些练习的目的是培养学生学习数学的能力，并帮助他们体验数学的乐趣。
Applications and Computers
应用程序和计算机[编辑]
The point of view taken here, that linear algebra is about vector spaces and linear maps, is not taken to the complete exclusion of others. Applications and the role of the computer are important and vital aspects of the subject. Consequently, each of this book's chapters closes with a few application or computerrelated topics. Some are: network flows, the speed and accuracy of computer linear reductions, Leontief Input/Output analysis, dimensional analysis, Markov chains, voting paradoxes, analytic projective geometry, and difference equations.
这里所采取的观点，即线性代数是关于向量空间和线性映射的，并不完全排除其他的观点。计算机的应用和作用是这门学科重要而重要的方面。因此，本书的每一章都以一些应用或计算机相关的主题结束。其中包括：网络流、计算机线性化简的速度和精度、Leontief输入/输出分析、量纲分析、马尔可夫链、投票悖论、解析射影几何和差分方程。
These topics are brief enough to be done in a day's class or to be given as independent projects for individuals or small groups. Most simply give the reader a taste of the subject, discuss how linear algebra comes in, point to some further reading, and give a few exercises. In short, these topics invite readers to see for themselves that linear algebra is a tool that a professional must have.
这些主题足够简短，可以在一天的课堂上完成，也可以作为个人或小组的独立项目。最简单的是让读者领略一下这个主题，讨论一下线性代数是如何产生的，指出一些进一步的阅读，并给出一些练习。简言之，这些主题邀请读者亲眼看到，线性代数是一个专业人士必须具备的工具。
For people reading this book on their own
对于自学这本书的人[编辑]
This book's emphasis on motivation and development make it a good choice for selfstudy. But, while a professional instructor can judge what pace and topics suit a class, if you are an independent student then perhaps you would find some advice helpful.
这本书对动机和发展的强调使它成为自学的好选择。但是，虽然专业的教师可以判断什么样的节奏和主题适合一节课，但如果你是一名独立学生，那么也许你会发现一些建议是有用的。
Here are two timetables for a semester. The first focuses on core material.
这是一个学期的两个时间表。第一个重点是核心材料。
week 星期  Monday 礼拜一  Wednesday 礼拜三  Friday 礼拜五 
1  One.I.1  One.I.1, 2  One.I.2, 3 
2  One.I.3  One.II.1  One.II.2 
3  One.III.1, 2  One.III.2  Two.I.1 
4  Two.I.2  Two.II  Two.III.1 
5  Two.III.1, 2  Two.III.2  Exam 
6  Two.III.2, 3  Two.III.3  Three.I.1 
7  Three.I.2  Three.II.1  Three.II.2 
8  Three.II.2  Three.II.2  Three.III.1 
9  Three.III.1  Three.III.2  Three.IV.1, 2 
10  Three.IV.2, 3, 4  Three.IV.4  Exam 
11  Three.IV.4, Three.V.1  Three.V.1, 2  Four.I.1, 2 
12  Four.I.3  Four.II  Four.II 
13  Four.III.1  Five.I  Five.II.1 
14  Five.II.2  Five.II.3  Review 
The second timetable is more ambitious (it supposes that you know One.II, the elements of vectors, usually covered in third semester calculus).
第二个时间表更具雄心（它假设你知道一、二，向量的元素，通常在第三学期微积分中讨论）。
week 星期  Monday 礼拜一  Wednesday 礼拜三  Friday 礼拜五 
1  One.I.1  One.I.2  One.I.3 
2  One.I.3  One.III.1, 2  One.III.2 
3  Two.I.1  Two.I.2  Two.II 
4  Two.III.1  Two.III.2  Two.III.3 
5  Two.III.4  Three.I.1  Exam 
6  Three.I.2  Three.II.1  Three.II.2 
7  Three.III.1  Three.III.2  Three.IV.1, 2 
8  Three.IV.2  Three.IV.3  Three.IV.4 
9  Three.V.1  Three.V.2  Three.VI.1 
10  Three.VI.2  Four.I.1  Exam 
11  Four.I.2  Four.I.3  Four.I.4 
12  Four.II  Four.II, Four.III.1  Four.III.2, 3 
13  Five.II.1, 2  Five.II.3  Five.III.1 
14  Five.III.2  Five.IV.1, 2  Five.IV.2 
See the table of contents for the titles of these subsections.
这些小节的标题见目录。
To help you make time tradeoffs, in the table of contents I have marked subsections as optional if some instructors will pass over them in favor of spending more time elsewhere. You might also try picking one or two topics that appeal to you from the end of each chapter. You'll get more from these if you have access to computer software that can do the big calculations.
为了帮助您进行时间权衡，在目录中，我将子部分标记为可选的，如果有些讲师会跳过它们，而将更多的时间花在其他地方。你也可以试着从每一章的结尾选一两个吸引你的话题。如果你能使用计算机软件进行大计算，你会从中得到更多。
The most important advice is: do many exercises. The recommended exercises are labeled throughout. (The answers are available.) You should be aware, however, that few inexperienced people can write correct proofs. Try to find a knowledgeable person to work with you on this.
最重要的建议是：多做练习。推荐的练习贯穿始终。（答案是有的）但是你应该知道，没有经验的人很少能写出正确的证明。试着找一个有见识的人和你一起工作。
Finally, if I may, a caution for all students, independent or not: I cannot overemphasize how much the statement that I sometimes hear, "I understand the material, but it's only that I have trouble with the problems" reveals a lack of understanding of what we are up to. Being able to do things with the ideas is their point. The quotes below express this sentiment admirably. They state what I believe is the key to both the beauty and the power of mathematics and the sciences in general, and of linear algebra in particular (I took the liberty of formatting them as poems).
最后，如果可以的话，我要提醒所有的学生，不管他们是否独立：我不能过分强调我有时听到的“我理解材料，但只是我对问题有困难”这句话多少暴露了我们对我们正在做的事情缺乏了解。能够用这些想法做事是他们的重点。下面的引文很好地表达了这种观点。它们陈述了我认为是数学和科学的美和力量的关键，尤其是线性代数（我冒昧地将它们格式化为诗歌）。
I know of no better tactic
than the illustration of exciting principles
by wellchosen particulars.
Stephen Jay Gould我知道没有比这更好的策略了
而不是那些令人兴奋的原则
通过精心挑选的细节。
史蒂芬·杰伊·古尔德
If you really wish to learn
then you must mount the machine
and become acquainted with its tricks
by actual trial.
Wilbur Wright如果你真的想学
那你就得装上机器
熟悉它的技巧
通过实际试验。
威尔伯·赖特
Jim Hefferon
Mathematics, Saint Michael's College
Colchester, Vermont USA 05439
http://joshua.smcvt.edu
2006May20
吉姆·赫弗伦 圣米歇尔学院数学系 美国佛蒙特州科尔切斯特市，邮编：05439
2006年5月20日
Author's Note.
Inventing a good exercise, one that enlightens as well as tests,
is a creative act, and hard work.
作者的笔记。创造一个好的练习，一个启发和测试，是一个创造性的行为，努力工作。
The inventor deserves recognition. But for some reason texts have traditionally not given attributions for questions. I have changed that here where I was sure of the source. I would greatly appreciate hearing from anyone who can help me to correctly attribute others of the questions.
这位发明家值得肯定。但由于某些原因，文本传统上没有给出问题的归属。我已经改变了这里我确定来源的地方。如果有人能帮助我正确回答其他问题，我将不胜感激。
第一章 Linear Systems/线性系统[编辑]
第一节[编辑]
Systems of linear equations are common in science and mathematics.
These two examples from high school science (O'Nan 1990)
give a sense of how they arise.
线性方程组在科学和数学中很常见。这两个来自《高中科学》（奥南1990）的例子让我们了解了它们是如何产生的。
The first example is from Physics.
Suppose that we are given three objects,
one with a mass known to be 2 kg, and are asked to find the unknown masses.
Suppose further that
experimentation with a meter stick produces these two balances.
第一个例子来自物理学。假设我们得到三个物体，一个质量已知为2公斤，被要求找出未知质量。进一步假设，使用仪表棒进行试验可以产生这两种平衡。
Since the sum of magnitudes of the torques of the clockwise forces equal those of the counter clockwise forces (the torque of an object rotating about a fixed origin is the cross product of the force on it and its position vector relative to the origin; gravitational acceleration is uniform we can divide both sides by it). The two balances give this system of two equations.
由于顺时针力的力矩大小之和等于逆时针力的大小（绕固定原点旋转的物体的力矩是其上的力与其相对于原点的位置矢量的叉积；重力加速度是均匀的，我们可以用它来划分两边）。这两个天平给出了这个由两个方程组成的系统。
The second example of a linear system is from Chemistry. We can mix, under controlled conditions, toluene and nitric acid to produce trinitrotoluene along with the byproduct water (conditions have to be controlled very well, indeed— trinitrotoluene is better known as TNT). In what proportion should those components be mixed? The number of atoms of each element present before the reaction
线性系统的第二个例子来自化学。我们可以在受控制的情况下混合甲苯（C₇H₈）和硝酸（HNO₃）的生产条件三硝基甲苯（C₇H₅O₆N₃）及其副产水（条件必须控制得很好，实际上，三硝基甲苯被称为TNT）。这些成分应该按多大比例混合？每种元素在反应前存在的原子数
must equal the number present afterward. Applying that principle to the elements C, H, N, and O in turn gives this system.
一定与反应后存在的原子数相等。根据这个反应中的C、H、N和O元素的守恒。
To finish each of these examples requires solving a system of equations. In each, the equations involve only the first power of the variables. This chapter shows how to solve any such system.
要完成这些例子中的每一个都需要解一个方程组。在每一个方程中，方程只涉及变量的一次方。本章介绍如何解决任何此类系统。
References[编辑]
 O'Nan, Micheal (1990), Linear Algebra (3rd ed.), Harcourt College Pub.
§ 1.1 Gauss' Method 高斯消元法[编辑]
 Definition 1.1
A linear equation in variables has the form
未知数的一个线性方程组，形如
where the numbers are the equation's coefficients and is the constant. An tuple is a solution of, or satisfies, that equation if substituting the numbers for the variables gives a true statement: .
其中是方程的系数，是一个常数项。 当一个元数组满足：成立 那么我们称这个数组为为方程的一个解。
A system of linear equations
一个线性方程组
has the solution if that tuple is a solution of all of the equations in the system.
当有一个元数组，是方程组中每一个方程的解，那么我们称 这个元数组为方程组的解。
 Example 1.2
The ordered pair is a solution of this system.
In contrast, is not a solution.
Finding the set of all solutions is solving the system. No guesswork or good fortune is needed to solve a linear system. There is an algorithm that always works. The next example introduces that algorithm, called Gauss' method. It transforms the system, step by step, into one with a form that is easily solved.
 Example 1.3
To solve this system
we repeatedly transform it until it is in a form that is easy to solve.
The third step is the only nontrivial one. We've mentally multiplied both sides of the first row by , mentally added that to the old second row, and written the result in as the new second row.
Now we can find the value of each variable. The bottom equation shows that . Substituting for in the middle equation shows that . Substituting those two into the top equation gives that and so the system has a unique solution: the solution set is .
Most of this subsection and the next one consists of examples of solving linear systems by Gauss' method. We will use it throughout this book. It is fast and easy. But, before we get to those examples, we will first show that this method is also safe in that it never loses solutions or picks up extraneous solutions.
 Theorem 1.4 (Gauss' method)
If a linear system is changed to another by one of these operations
 an equation is swapped with another
 an equation has both sides multiplied by a nonzero constant
 an equation is replaced by the sum of itself and a multiple of another
then the two systems have the same set of solutions.
Each of those three operations has a restriction. Multiplying a row by is not allowed because obviously that can change the solution set of the system. Similarly, adding a multiple of a row to itself is not allowed because adding times the row to itself has the effect of multiplying the row by . Finally, swapping a row with itself is disallowed to make some results in the fourth chapter easier to state and remember (and besides, selfswapping doesn't accomplish anything).
 Proof
We will cover the equation swap operation here and save the other two cases for Problem 14.
Consider this swap of row with row .
The tuple satisfies the system before the swap if and only if substituting the values, the 's, for the variables, the 's, gives true statements:
and ...
and ...
and ... .
In a requirement consisting of statements anded together we can rearrange the order of the statements, so that this requirement is met if and only if and ... and ... and ... . This is exactly the requirement that solves the system after the row swap.
 Definition 1.5
The three operations from Theorem 1.4 are the elementary reduction operations, or row operations, or Gaussian operations. They are swapping, multiplying by a scalar or rescaling, and pivoting.
When writing out the calculations, we will abbreviate "row " by "". For instance, we will denote a pivot operation by , with the row that is changed written second. We will also, to save writing, often list pivot steps together when they use the same .
 Example 1.6
A typical use of Gauss' method is to solve this system.
The first transformation of the system involves using the first row to eliminate the in the second row and the in the third. To get rid of the second row's , we multiply the entire first row by , add that to the second row, and write the result in as the new second row. To get rid of the third row's , we multiply the first row by , add that to the third row, and write the result in as the new third row.
(Note that the two steps and are written as one operation.) In this second system, the last two equations involve only two unknowns. To finish we transform the second system into a third system, where the last equation involves only one unknown. This transformation uses the second row to eliminate from the third row.
Now we are set up for the solution. The third row shows that . Substitute that back into the second row to get , and then substitute back into the first row to get .
 Example 1.7
For the Physics problem from the start of this chapter, Gauss' method gives this.
So , and backsubstitution gives that . (The Chemistry problem is solved later.)
 Example 1.8
The reduction
shows that , , and .
As these examples illustrate, Gauss' method uses the elementary reduction operations to set up backsubstitution.
 Definition 1.9
In each row, the first variable with a nonzero coefficient is the row's leading variable. A system is in echelon form if each leading variable is to the right of the leading variable in the row above it (except for the leading variable in the first row).
 Example 1.10
The only operation needed in the examples above is pivoting. Here is a linear system that requires the operation of swapping equations. After the first pivot
the second equation has no leading . To get one, we look lower down in the system for a row that has a leading and swap it in.
(Had there been more than one row below the second with a leading then we could have swapped in any one.) The rest of Gauss' method goes as before.
Backsubstitution gives , , , and .
Strictly speaking, the operation of rescaling rows is not needed to solve linear systems. We have included it because we will use it later in this chapter as part of a variation on Gauss' method, the GaussJordan method.
All of the systems seen so far have the same number of equations as unknowns. All of them have a solution, and for all of them there is only one solution. We finish this subsection by seeing for contrast some other things that can happen.
 Example 1.11
Linear systems need not have the same number of equations as unknowns. This system
has more equations than variables. Gauss' method helps us understand this system also, since this
shows that one of the equations is redundant. Echelon form
gives and . The "" is derived from the redundancy.
That example's system has more equations than variables. Gauss' method is also useful on systems with more variables than equations. Many examples are in the next subsection.
Another way that linear systems can differ from the examples shown earlier is that some linear systems do not have a unique solution. This can happen in two ways.
The first is that it can fail to have any solution at all.
 Example 1.12
Contrast the system in the last example with this one.
Here the system is inconsistent: no pair of numbers satisfies all of the equations simultaneously. Echelon form makes this inconsistency obvious.
The solution set is empty.
 Example 1.13
The prior system has more equations than unknowns, but that is not what causes the inconsistency— Example 1.11 has more equations than unknowns and yet is consistent. Nor is having more equations than unknowns sufficient for inconsistency, as is illustrated by this inconsistent system with the same number of equations as unknowns.
The other way that a linear system can fail to have a unique solution is to have many solutions.
 Example 1.14
In this system
any pair of numbers satisfying the first equation automatically satisfies the second. The solution set is infinite; some of its members are , , and . The result of applying Gauss' method here contrasts with the prior example because we do not get a contradictory equation.
Don't be fooled by the "" equation in that example. It is not the signal that a system has many solutions.
 Example 1.15
The absence of a "" does not keep a system from having many different solutions. This system is in echelon form
has no "", and yet has infinitely many solutions. (For instance, each of these is a solution: , , , and . There are infinitely many solutions because any triple whose first component is and whose second component is the negative of the third is a solution.)
Nor does the presence of a "" mean that the system must have many solutions. Example 1.11 shows that. So does this system, which does not have many solutions— in fact it has none— despite that when it is brought to echelon form it has a "" row.
We will finish this subsection with a summary of what we've seen so far about Gauss' method.
Gauss' method uses the three row operations to set a system up for back substitution. If any step shows a contradictory equation then we can stop with the conclusion that the system has no solutions. If we reach echelon form without a contradictory equation, and each variable is a leading variable in its row, then the system has a unique solution and we find it by back substitution. Finally, if we reach echelon form without a contradictory equation, and there is not a unique solution (at least one variable is not a leading variable) then the system has many solutions.
The next subsection deals with the third case— we will see how to describe the solution set of a system with many solutions.
Exercises[编辑]
Template:Linear Algebra/Book 2/Recommended
 Problem 1
Use Gauss' method to find the unique solution for each system.
Template:Linear Algebra/Book 2/Recommended
 Problem 2
Use Gauss' method to solve each system or conclude "many solutions" or "no solutions".
Template:Linear Algebra/Book 2/Recommended
 Problem 3
There are methods for solving linear systems other than Gauss' method. One often taught in high school is to solve one of the equations for a variable, then substitute the resulting expression into other equations. That step is repeated until there is an equation with only one variable. From that, the first number in the solution is derived, and then backsubstitution can be done. This method takes longer than Gauss' method, since it involves more arithmetic operations, and is also more likely to lead to errors. To illustrate how it can lead to wrong conclusions, we will use the system
from Example 1.12.
 Solve the first equation for and substitute that expression into the second equation. Find the resulting .
 Again solve the first equation for , but this time substitute that expression into the third equation. Find this .
What extra step must a user of this method take to avoid erroneously concluding a system has a solution?
Template:Linear Algebra/Book 2/Recommended
 Problem 4
For which values of are there no solutions, many solutions, or a unique solution to this system?
Template:Linear Algebra/Book 2/Recommended
 Problem 5
This system is not linear, in some sense,
and yet we can nonetheless apply Gauss' method. Do so. Does the system have a solution?
Template:Linear Algebra/Book 2/Recommended
 Problem 6
What conditions must the constants, the 's, satisfy so that each of these systems has a solution? Hint. Apply Gauss' method and see what happens to the right side (Anton 1987).
 Problem 7
True or false: a system with more unknowns than equations has at least one solution. (As always, to say "true" you must prove it, while to say "false" you must produce a counterexample.)
 Problem 8
Must any Chemistry problem like the one that starts this subsection— a balance the reaction problem— have infinitely many solutions?
Template:Linear Algebra/Book 2/Recommended
 Problem 9
Find the coefficients , , and so that the graph of passes through the points , , and .
 Problem 10
Gauss' method works by combining the equations in a system to make new equations.
 Can the equation be derived, by a sequence of
Gaussian reduction steps, from the equations in this system?
 Can the equation be derived, by a sequence of
Gaussian reduction steps, from the equations in this system?
 Can the equation be derived,
by a sequence of
Gaussian reduction steps, from the equations in the system?
 Problem 11
Prove that, where are real numbers and , if
has the same solution set as
then they are the same equation. What if ?
Template:Linear Algebra/Book 2/Recommended
 Problem 12
Show that if then
has a unique solution.
Template:Linear Algebra/Book 2/Recommended
 Problem 13
In the system
each of the equations describes a line in the plane. By geometrical reasoning, show that there are three possibilities: there is a unique solution, there is no solution, and there are infinitely many solutions.
 Problem 14
Finish the proof of Theorem 1.4.
 Problem 15
Is there a twounknowns linear system whose solution set is all of ?
Template:Linear Algebra/Book 2/Recommended
 Problem 16
Are any of the operations used in Gauss' method redundant? That is, can any of the operations be synthesized from the others?
 Problem 17
Prove that each operation of Gauss' method is reversible. That is, show that if two systems are related by a row operation then there is a row operation to go back .
 ? Problem 18
A box holding pennies, nickels and dimes contains thirteen coins with a total value of cents. How many coins of each type are in the box? (Anton 1987)
 ? Problem 19
Four positive integers are given. Select any three of the integers, find their arithmetic average, and add this result to the fourth integer. Thus the numbers 29, 23, 21, and 17 are obtained. One of the original integers is:
 19
 21
 23
 29
 17
(Salkind 1975, 1955 problem 38)
Template:Linear Algebra/Book 2/Recommended
 ? Problem 20
Laugh at this: . It resulted from substituting a code letter for each digit of a simple example in addition, and it is required to identify the letters and prove the solution unique (Ransom & Gupta 1935).
 ? Problem 21
The Wohascum County Board of Commissioners, which has 20 members, recently had to elect a President. There were three candidates (, , and ); on each ballot the three candidates were to be listed in order of preference, with no abstentions. It was found that 11 members, a majority, preferred over (thus the other 9 preferred over ). Similarly, it was found that 12 members preferred over . Given these results, it was suggested that should withdraw, to enable a runoff election between and . However, protested, and it was then found that 14 members preferred over ! The Board has not yet recovered from the resulting confusion. Given that every possible order of , , appeared on at least one ballot, how many members voted for as their first choice (Gilbert, Krusemeyer & Larson 1993, Problem number 2)?
 ? Problem 22
"This system of linear equations with unknowns," said the Great Mathematician, "has a curious property."
"Good heavens!" said the Poor Nut, "What is it?"
"Note," said the Great Mathematician, "that the constants are in arithmetic progression."
"It's all so clear when you explain it!" said the Poor Nut. "Do you mean like and ?"
"Quite so," said the Great Mathematician, pulling out his bassoon. "Indeed, even larger systems can be solved regardless of their progression. Can you find their solution?"
"Good heavens!" cried the Poor Nut, "I am baffled."
Are you? (Dudley, Lebow & Rothman 1963)
§ 1.2 Describing the Solution Set 解集的表示[编辑]
A linear system with a unique solution has a solution set with one element. A linear system with no solution has a solution set that is empty. In these cases the solution set is easy to describe. Solution sets are a challenge to describe only when they contain many elements.
 Example 2.1
This system has many solutions because in echelon form
not all of the variables are leading variables. The Gauss' method theorem showed that a triple satisfies the first system if and only if it satisfies the third. Thus, the solution set can also be described as . However, this second description is not much of an improvement. It has two equations instead of three, but it still involves some hardtounderstand interaction among the variables.
To get a description that is free of any such interaction, we take the variable that does not lead any equation, , and use it to describe the variables that do lead, and . The second equation gives and the first equation gives . Thus, the solution set can be described as . For instance, is a solution because taking gives a first component of and a second component of .
The advantage of this description over the ones above is that the only variable appearing, , is unrestricted — it can be any real number.
 Definition 2.2
The nonleading variables in an echelonform linear system are free variables.
In the echelon form system derived in the above example, and are leading variables and is free.
 Example 2.3
A linear system can end with more than one variable free. This row reduction
ends with and leading, and with both and free. To get the description that we prefer we will start at the bottom. We first express in terms of the free variables and with . Next, moving up to the top equation, substituting for in the first equation and solving for yields . Thus, the solution set is .
We prefer this description because the only variables that appear, and , are unrestricted. This makes the job of deciding which fourtuples are system solutions into an easy one. For instance, taking and gives the solution . In contrast, is not a solution, since the first component of any solution must be minus twice the third component plus twice the fourth.
 Example 2.4
After this reduction
lead, are free. The solution set is . For instance, satisfies the system — take and . The fourtuple is not a solution since its first coordinate does not equal its second.
We refer to a variable used to describe a family of solutions as a parameter and we say that the set above is parametrized with and . (The terms "parameter" and "free variable" do not mean the same thing. Above, and are free because in the echelon form system they do not lead any row. They are parameters because they are used in the solution set description. We could have instead parametrized with and by rewriting the second equation as . In that case, the free variables are still and , but the parameters are and . Notice that we could not have parametrized with and , so there is sometimes a restriction on the choice of parameters. The terms "parameter" and "free" are related because, as we shall show later in this chapter, the solution set of a system can always be parametrized with the free variables. Consequently, we shall parametrize all of our descriptions in this way.)
 Example 2.5
This is another system with infinitely many solutions.
The leading variables are . The variable is free. (Notice here that, although there are infinitely many solutions, the value of one of the variables is fixed — .) Write in terms of with . Then . To express in terms of , substitute for into the first equation to get . The solution set is .
We finish this subsection by developing the notation for linear systems and their solution sets that we shall use in the rest of this book.
 Definition 2.6
An matrix is a rectangular array of numbers with rows and columns. Each number in the matrix is an entry.
Matrices are usually named by upper case roman letters, e.g. . Each entry is denoted by the corresponding lowercase letter, e.g. is the number in row and column of the array. For instance,
has two rows and three columns, and so is a matrix. (Read that "twobythree"; the number of rows is always stated first.) The entry in the second row and first column is . Note that the order of the subscripts matters: since . (The parentheses around the array are a typographic device so that when two matrices are side by side we can tell where one ends and the other starts.)
Matrices occur throughout this book. We shall use to denote the collection of matrices.
 Example 2.7
We can abbreviate this linear system
with this matrix.
The vertical bar just reminds a reader of the difference between the coefficients on the systems's left hand side and the constants on the right. When a bar is used to divide a matrix into parts, we call it an augmented matrix. In this notation, Gauss' method goes this way.
The second row stands for and the first row stands for so the solution set is . One advantage of the new notation is that the clerical load of Gauss' method — the copying of variables, the writing of 's and 's, etc. — is lighter.
We will also use the array notation to clarify the descriptions of solution sets. A description like from Example 2.3 is hard to read. We will rewrite it to group all the constants together, all the coefficients of together, and all the coefficients of together. We will write them vertically, in onecolumn wide matrices.
For instance, the top line says that . The next section gives a geometric interpretation that will help us picture the solution sets when they are written in this way.
 Definition 2.8
A vector (or column vector) is a matrix with a single column. A matrix with a single row is a row vector. The entries of a vector are its components.
Vectors are an exception to the convention of representing matrices with capital roman letters. We use lowercase roman or greek letters overlined with an arrow: ... or ... (boldface is also common: or ). For instance, this is a column vector with a third component of .
 Definition 2.9
The linear equation with unknowns is satisfied by
if . A vector satisfies a linear system if it satisfies each equation in the system.
The style of description of solution sets that we use involves adding the vectors, and also multiplying them by real numbers, such as the and . We need to define these operations.
 Definition 2.10
The vector sum of and is this.
In general, two matrices with the same number of rows and the same number of columns add in this way, entrybyentry.
 Definition 2.11
The scalar multiplication of the real number and the vector is this.
In general, any matrix is multiplied by a real number in this entrybyentry way.
Scalar multiplication can be written in either order: or , or without the "" symbol: . (Do not refer to scalar multiplication as "scalar product" because that name is used for a different operation.)
 Example 2.12
Notice that the definitions of vector addition and scalar multiplication agree where they overlap, for instance, .
With the notation defined, we can now solve systems in the way that we will use throughout this book.
 Example 2.13
This system
reduces in this way.
The solution set is . We write that in vector form.
Note again how well vector notation sets off the coefficients of each parameter. For instance, the third row of the vector form shows plainly that if is held fixed then increases three times as fast as .
That format also shows plainly that there are infinitely many solutions. For example, we can fix as , let range over the real numbers, and consider the first component . We get infinitely many first components and hence infinitely many solutions.
Another thing shown plainly is that setting both to 0 gives that this
is a particular solution of the linear system.
 Example 2.14
In the same way, this system
reduces
to a oneparameter solution set.
Before the exercises, we pause to point out some things that we have yet to do.
The first two subsections have been on the mechanics of Gauss' method. Except for one result, Theorem 1.4— without which developing the method doesn't make sense since it says that the method gives the right answers— we have not stopped to consider any of the interesting questions that arise.
For example, can we always describe solution sets as above, with a particular solution vector added to an unrestricted linear combination of some other vectors? The solution sets we described with unrestricted parameters were easily seen to have infinitely many solutions so an answer to this question could tell us something about the size of solution sets. An answer to that question could also help us picture the solution sets, in , or in , etc.
Many questions arise from the observation that Gauss' method can be done in more than one way (for instance, when swapping rows, we may have a choice of which row to swap with). Theorem 1.4 says that we must get the same solution set no matter how we proceed, but if we do Gauss' method in two different ways must we get the same number of free variables both times, so that any two solution set descriptions have the same number of parameters? Must those be the same variables (e.g., is it impossible to solve a problem one way and get and free or solve it another way and get and free)?
In the rest of this chapter we answer these questions. The answer to each is "yes". The first question is answered in the last subsection of this section. In the second section we give a geometric description of solution sets. In the final section of this chapter we tackle the last set of questions. Consequently, by the end of the first chapter we will not only have a solid grounding in the practice of Gauss' method, we will also have a solid grounding in the theory. We will be sure of what can and cannot happen in a reduction.
Exercises[编辑]
Template:Linear Algebra/Book 2/Recommended
 Problem 1
Find the indicated entry of the matrix, if it is defined.
Template:Linear Algebra/Book 2/Recommended
 Problem 2
Give the size of each matrix.
Template:Linear Algebra/Book 2/Recommended
 Problem 3
Do the indicated vector operation, if it is defined.
Template:Linear Algebra/Book 2/Recommended
 Problem 4
Solve each system using matrix notation. Express the solution using vectors.
Template:Linear Algebra/Book 2/Recommended
 Problem 5
Solve each system using matrix notation. Give each solution set in vector notation.
Template:Linear Algebra/Book 2/Recommended
 Problem 6
The vector is in the set. What value of the parameters produces that vector?
 ,
 ,
 ,
 Problem 7
Decide if the vector is in the set.
 ,
 ,
 ,
 ,
 Problem 8
Parametrize the solution set of this oneequation system.
Template:Linear Algebra/Book 2/Recommended
 Problem 9
 Apply Gauss' method to the lefthand side to solve
 Use your answer from the prior part to solve this.
Template:Linear Algebra/Book 2/Recommended
 Problem 10
Why is the comma needed in the notation "" for matrix entries?
Template:Linear Algebra/Book 2/Recommended
 Problem 11
Give the matrix whose th entry is
 ;
 to the power.
 Problem 12
For any matrix , the transpose of , written , is the matrix whose columns are the rows of . Find the transpose of each of these.
Template:Linear Algebra/Book 2/Recommended
 Problem 13
 Describe all functions such that and .
 Describe all functions such that .
 Problem 14
Show that any set of five points from the plane lie on a common conic section, that is, they all satisfy some equation of the form where some of are nonzero.
 Problem 15
Make up a four equations/four unknowns system having
 a oneparameter solution set;
 a twoparameter solution set;
 a threeparameter solution set.
 ? Problem 16
 Solve the system of equations.
 Answer the above question for the system.
 ? Problem 17
In air a goldsurfaced sphere weighs grams. It is known that it may contain one or more of the metals aluminum, copper, silver, or lead. When weighed successively under standard conditions in water, benzene, alcohol, and glycerine its respective weights are , , , and grams. How much, if any, of the forenamed metals does it contain if the specific gravities of the designated substances are taken to be as follows?
Aluminum  2.7  Alcohol  0.81  
Copper  8.9  Benzene  0.90  
Gold  19.3  Glycerine  1.26  
Lead  11.3  Water  1.00  
Silver  10.8 