# Matrices and Linear Algebra (Dover Books on Mathematics) [2nd Revised ed.] 0486660141, 9780486660141

##### Linear algebra is one of the central disciplines in mathematics. A student of pure mathematics must know linear algebra

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English Pages 302 Year 1989

Preface to the Second Edition
Preface to the First Edition
CHAPTER 1 THE ALGEBRA OF MATRICES
1. MATRICES: DEFINITIONS
2. ADDITION AND SCALAR MULTIPLICATION OF MATRICES
3. MATRIX MULTIPLICATION
4. SQUARE MATRICES, INVERSES, AND ZERO DIVISORS
5. TRANSPOSES, PARTITIONING OF MATRICES, AND DIRECT SUMS
CHAPTER 2 LINEAR EQUATIONS
1. EQUIVALENT SYSTEMS OF EQUATIONS
2. ROW OPERATIONS ON MATRICES
3. ROW ECHELON FORM
5. THE UNRESTRICTED CASE: A CONSISTENCY CONDITION
6. THE UNRESTRICTED CASE: A GENERAL SOLUTION
7. INVERSES OF NONSINGULAR MATRICES
CHAPTER 3 VECTOR SPACES
1. VECTORS AND VECTOR SPACES
2. SUBSPACES AND LINEAR COMBINATIONS
3. LINEAR DEPENDENCE AND LINEAR INDEPENDENCE
6. ROW SPACES OF MATRICES
9. EQUIVALENCE RELATIONS AND CANONICAL FORMS OF MATRICES
CHAPTER 4 DETERMINANTS
1. INTRODUCTION AS A VOLUME FUNCTION
2. PERMUTATIONS AND PERMUTATION MATRICES
4. PRACTICAL EVALUATION AND TRANSPOSES OF DETERMINANTS
6. DETERMINANTS AND RANKS
CHAPTER 5 LINEAR TRANSFORMATIONS
1. DEFINITIONS
2. REPRESENTATION OF LINEAR TRANSFORMATIONS
3. REPRESENTATIONS UNDER CHANGE OF BASES
CHAPTER 6 EIGENVALUES AND EIGENVECTORS
1. INTRODUCTION
2. RELATION BETWEEN EIGENVALUES AND MINORS
3. SIMILARITY
4. ALGEBRAIC AND GEOMETRIC MULTIPLICITIES
5. JORDAN CANONICAL FORM
6. FUNCTIONS OF MATRICES
7. APPLICATION: MARKOV CHAINS
CHAPTER 7 INNER PRODUCT SPACES
1. INNER PRODUCTS
2. REPRESENTATION OF INNER PRODUCTS
3. ORTHOGONAL BASES
4. UNITARY EQUIVALENCE AND HERMITIAN MATRICES
5. CONGRUENCE AND CONJUNCTIVE EQUIVALENCE
7. THE NATURAL INVERSE
8. NORMAL MATRICES
CHAPTER 8 APPLICATIONS TO DIFFERENTIAL EQUATIONS
1. INTRODUCTION
2. HOMOGENEOUS DIFFERENTIAL EQUATIONS
3. LINEAR DIFFERENTIAL EQUATIONS: THE UNRESTRICTED CASE
4. LINEAR OPERATORS: THE GLOBAL VIEW
Symbols
Index
• Commentary
• There're some missing pages, watch out for them!
##### Citation preview

MATRICES and UNEAR ALGEBRA second edition

Hans Schneider James Joseph Sylvester Professor of Mathematics University of Wisconsin-Madison

George Phillip Barker University of Missouri-Kansas City

DOVER PUBLICATIONS, INC., New York

rights

reserved

under

Pan

American

and International

Conventions. Published in Canada by General Publishing Company, Ltd., 30 Lesmill Road, Don Mills, Toronto, Ontario. Published in the United Kingdom by Constable and Company, Ltd., 10 Orange Street, London WC2H 7EG. T his Dover edition, first published in 1989, is an unabridged, slightly corrected republication of the second edition (1973) of the work originally published by Holt, Rinehart and Winston, Inc., New York, in 1968. Manufactured in the United States of America Dover Publications, Inc., 31 East 2nd Street, Mineola, N. Y. 11501 Library of Congress Cataloging-in-Publication Data

Schneider, Hans Matrices and linear algebra / H ans Schneider, George Phillip Barker. p.

cm.

Reprint. Originally p ublished: 2nd ed. New Y or k: H olt, Rinehart and Winston, 1973. Includes index. ISBN 0-486-66014-1 I. Algebras, Linear. 2. Matrices. I. Barker, George Phillip. II. Title.

[QA184.S38 1989] 512.9'434-dc l 9

89-30966 CIP

preface to the

second edition The primary difference between this new edition and the first one is the addition of several exercises in each chapter and a brand new section in Chapter 7. The exercises, which are both true-false and multiple choice, will enable the student to test his grasp of the definitions and theorems in the chapter. The new section in Chapter 7 illustrates the geometric con­ tent of Sylvester's Theorem by means of conic sections and quadric surfaces. We would also like to thank the correspondents and students who have brought to our attention various misprints in the first edition that we have corrected in this edition . MADISON, WISCONSIN KANSAS CITY, MISSOURI OCTOBER 1972

v

H.S. G.P.B.

preface to the

first edition Linear algebra is now one of the central disciplines in mathematics. A student of pure mathematics must know linear algebra if he is to continue with modern algebra or functional analysis. Much of the mathematics now taught to en gineers and physicists requires it. It is for this reason that the Committee on Underg raduate Programs in Mathematics recommends that linear algebra be taught early in the under­ graduate curriculum. In this book, written mainly for students in physics, engineering, economics, and other fields outside mathematics, we attempt to make the subject accessible to a sophomore or even a freshman student with little mathemati­ cal experience. After a short introduction to matrices in Chapter 1, we deal with the solving of linear equations in Chapter 2. We then use the insight gained there to motivate the study of abstract vector spaces in Chapter 3. Chapter 4 deals with determinants. Here we give an axiomatic definition, but quickly develop the determinant as a si gned sum of products. For the last thirty years there has been a vigorous and sometimes acrimonious discussion between the proponents of matrices and those of linear transformation. The con­ troversy now appears somewhat absurd, since the level of abstraction that is appropriate is surely determined by the mathematical goal. Thus, if one is aiming to generalize toward ring theory, one should evidently stress linear transformations. On the other hand, if one is looking for the linear algebra analogue of the classical inequalities, then clearly matrices VII

VIII

PREFACE

form the natural setting. From a pedagogical point of view, it seems appropriate to us, in the case of sophomore students, first to deal with matrices. We turn to linear transformations in Chapter 5. In Chapter 6, which deals with eigenvalues and similarity, we do some rapid switching between the matrix and the linear transformation points of view. We use whichever approach seems better at any given time. We feel that a stu­ dent of linear algebra must acquire the skill of switching from one point of view to another to become proficient in this field. Chapter 7 deals with inner product spaces. In Chapter 8 we deal with systems of linear differential equations. Obviously, for this chapter (and this chapter only) calculus is a prerequisite. There are at least two good reasons for including some linear differential equations in this linear algebra book. First, a student whose only model for a linear transformation is a matrix does not see why the abstract approach is desirable at all. If he is shown that certain differential operators are linear transformations also, then the point of abstraction becomes much more meaningful. Second, the kind of student we have in mind must become familiar with linear differential equations at some stage in his career, and quite often he is aware of this. We have found in teaching this course at the University of Wisconsin that the promise that the subject we are teaching can be applied to differential equations will motivate some students strongly. We gratefully acknowledge support from the National Science Founda­ tion under the auspices of the Committee on Undergraduate Programs in Mathematics for producing some preliminary notes in linear algebra. These notes were produced by Ken Casey and Ken Kapp, to whom thanks are also due. Some problems were supplied by Leroy Dickey and Peter Smith. Steve Bauman has taught from a preliminary version of this book, and we thank him for suggesting some improvements. We should also like to thank our publishers, Holt, Rinehart and Winston, and their mathematics editor, Robert M. Thrall. His remarks and criti­ cisms have helped us to improve this book. MADISON, WISCONSIN JANUARY 1968

H.S. G.P.B.

contents

Preface to the Second Edition

v

Preface to the First Edition

vii

THE ALGEBRA OF MATRICES

l. 2.

MATRICES:

]

DEFINITIONS

7

MATRICES

3.

MATRIX MULTIPLICATION

12

4.

SQUARE MATRICES, INVERSES, AND ZERO DIVISORS

23

5.

TRANSPOSES, PARTITIONING OF MATRICES,

30

AND DIRECT SUMS

2

LINEAR EQUATIONS

l.

EQUIVALENT SYSTEMS OF EQUATIONS

42

2.

ROW OPERATIONS ON MATRICES

47

3.

ROW ECHELON FORM

57

4.

HOMOGENEOUS SYSTEMS OF EQUATIONS

63

5.

THE UNRESTRICTED CASE:

A CONSISTENCY

74

CONDITION

IX

6.

THE UNRESTRICTED CASE:

7.

INVERSES OF NONSINGULAR MATRICES

A GENERAL SOLUTION

79 88

x

CONTENTS

3

VECTOR SPACES I.

VECTORS AND VECTOR SPACES

2.

SUBSPACES AND LINEAR COMBINATIONS

100

3.

LINEAR DEPENDENCE AND LINEAR INDEPENDENCE

112

4.

BASES

119

5.

BASES AND REPRESENTATIONS

133

6.

ROW SPACES OF MATRICES

140

7.

COLUMN EQUIVALENCE

147

8.

ROW-COLUMN EQUIVALENCE

151

9.

EQUIVALENCE RELATIONS AND CANONICAL FORMS OF MATRICES

4

I.

INTRODUCTION AS A VOLUME FUNCTION

161

2.

PERMUTATIONS AND PERMUTATION MATRICES

172

3.

UNIQUENESS AND EXISTENCE OF THE

4.

6

156

DETERMINANTS

DETERMINANT FUNCTION

5

96

181

PRACTICAL EVALUATION AND TRANSPOSES OF DETERMINANTS

188

5.

193

6.

DETERMINANTS AND RANKS

206

LINEAR TRANSFORMATIONS I.

DEFINITIONS

211

2.

REPRESENTATION OF LINEAR TRANSFORMATIONS

217

3.

REPRESENTATIONS UNDER CHANGE OF BASES

232

EIGENVALUES AND EIGENVECTORS I.

INTRODUCTION

2.

RELATION BETWEEN EIGENVALUES AND MINORS

248

3.

SIMILARITY

259

239

XI

CONTENTS

7

8

4.

ALGEBRAIC AND GEOMETRIC MULTIPLICITIES

267

5.

JORDAN CANONICAL FORM

272

6.

FUNCTIONS OF MATRICES

276

7.

APPLICATION:

292

MARKOV CHAINS

INNER PRODUCT SPACES

I.

INNER PRODUCTS

304

2.

REPRESENTATION OF INNER PRODUCTS

308

3.

ORTHOGONAL BASES

317

4.

UNITARY EQUIVALENCE AND HERMITIAN MATRICES

325

5.

CONGRUENCE AND CONJUNCTIVE EQUIVALENCE

336

6.

344

7.

THE NATURAL INVERSE

348

8.

NORMAL MATRICES

354

APPLICATIONS TO DIFFERENTIAL EQUATIONS

I.

INTRODUCTION

362

2.

HOMOGENEOUS DIFFERENTIAL EQUATIONS

365

3.

LINEAR DIFFERENTIAL EQUATIONS:

4.

THE UNRESTRICTED CASE

372

LINEAR OPERATORS:

377

THE GLOBAL VIEW

381

Symbols

407

Index

409

MATRICES and UNEAR ilGEBRA

chapter

J

The Algebra of Matrices

1.

MATRICES: DEFINITIONS This book is entitled

Matrices

and

Linear Algebra,

and

"linear" will be the most common mathematical term used here. This word has many related meanings, and now we shall explain what a linear equation is. An example of a linear equa­ tion is 3x1

+

2x2

=

5, where x1 and x2 are unknowns. In

general an equation is called linear if it is of the form

(1.1.1) where x1,

·

·

·

, Xn

are unknowns, and

a1,

·

·

·

,

an

and

b

are

numbers. Observe that in a linear equation, products such as x1x2 or x34 and more general functions such as sin x1 do not occur. In elementary books a pair of equations such as

{

(1.1.2)

3x1 - 2x2 -xi

+

+

5x2

4x3

= .

1

=

-3

is called a pair of simultaneous equations. We shall call such a pair a

system

of linear equations. Of course we may have

more than three unknowns and more than two equations. Thus the most general system of

m equations inn unknowns is

2

THE ALGEBRA

OF

MATRICES G11X1

+

GmJXI

+

·

·

·

+

GJnXn

+

GmnXn

=

b1

(1.1.3) =

The a;j are numbers, and the subscript (i,

bm.

/) denotes that a;j is the

coefficient of Xj in the ith equation. So far we have not explained what the coefficients of the unknowns are, but we have taken for granted that they are real numbers such as

...J2,

or

1r.

2,

The coefficients could just as well be complex numbers. This

case would arise if we considered the equations

ix1 - (2 + i)x2

=

I

2x1 + (2 - i)x2

=

-i

Xt

+

2X2

=

3.

Note that a real number is also a complex number (with imaginary part zero), but sometimes it is important to consider either all real numbers or all complex numbers. We shall denote the real numbers by

R and the complex numbers by C. The reader who is familiar with R and C are fields. In fact, most of our

abstract algebra will note that

results could be stated for arbitrary fields. (A reader unfamiliar with abstract algebra should ignore the previous two sentences.) Although we are not concerned with such generality, to avoid stating most theorems

R C. Of course we must be consistent in any

twice we shall use the symbol F to stand for either the real numbers or the complex numbers

particular theorem. Thus in any one theorem if F stands for the real numbers in any place, it must stand for the real numbers in all places. Where convenient we shall call Fa number system. In Chapter 2 we shall study systems of linear equations in greater detail. In this chapter we shall use linear equations only to motivate the concept of a matrix. Matrices will turn out to be extremely useful -not only in the study of linear equations but also in much else. If we consider the system of equations

(l.l.2), we see that the arrays

of coefficients

[_� -� �] [_�J

4

THE ALGEBRA OF MATRICES

is called the ith row of

A

and we shall denote it by a;•. Similarly, the

vertical array

Omj

is called thejth column of A, and we shall often denote it by a.1. Observe

A occurring in the ith row and the jth column. A has m rows and n columns it is called an m X n matrix.

that aij is the entry of If the matrix

In particular, if m

=

n the matrix is called a square matrix. At times an

n X n square matrix is referred to as a matrix of order n. Two other special cases are the m X I matrix, referred to as a column vector, and the I X n matrix, which is called a row vector. Examples of each special case are

[�]

(2

'Tri

-% +

Usually we denote matrices by capital letters

(A,

i/5].

B, and C), but some­

times the symbols [a;1], [bk1], and [cpq] are used. The entries of the matrix

A

will be denoted by aij, those of Bby bk1, and so forth. It is important to realize that

are all distinct matrices. To emphasize this point, we make the following

(1.1.6)

DEFINITION

matrix. Then

A

=

Let

A

be an m X n matrix and Ba p X q

Bif and only if m i

=

l,

·

·

· ,

=

p, n

m,

j

=

=

q, and 1,

· · ·,

n.

Thus for each pair of integers m, n we may consider two sets of matrices. One is the set of all m X n matrices with entries from the set of real numbers

R,

and the other is the set of all m X

n

matrices with

6

THE ALGEBRA OF MATRICES

Then

Ax

b is shorthand for

=

i (2 + i)x1 + Ox2 + (2 - i)x3 - 7X4 = - 2

3 we shall see that the left side of (l.l.7) may be read as the x are column vectors; b is a column vector with m elements and x is a column vector with n ele­ In Section

product of two matrices. Note that b and

ments. This method of writing the linear equations concentrates attention upon the essential item, the coefficient array.

EXERCISES

l. Find the matrices (A, b, x), corresponding to the following systems of equations. (a)

2x1 - 3x2 =

(b) 7x1 + 3x2 - X3 = 7

4

4x1 + 2x2 = -6.

X1 + X2

8

19x2 - X3 =17.

(c)

2x + 3y - 5z + 7w =11

- 4w =16

(d) 2x + 3y

y + 4z

z + w = 5.

=6

z + 5w = 8

6x

(e) (3 + 2i)z1 + (-2 + 4i)z2 =2 + i

=7

+ 7w =9.

(4 + 4i)z1 + (-7 + 7i)z2 =4 - i.

(f ) 3z1 + (4 - 4i)z2 = 6 Zt

+ (2 + 2i)z2 = 7 - i.

2. What systems of equations correspond to the following pairs of matrices? a

( ) A =

[ : �]. [ ;]· 1

-2

b

'1T

=

-.J2

(b)

A=[:

7 2.

2.

ADDITION AND SCALAR MULTIPLICATION OF MATRICES

ADDITION AND SCALAR MULTI PLICATION OF MATRICES We wish to see the effect on their corresponding matrices of adding two systems of equations. Consider the following two systems of equa­ tions with their corresponding matrices:

{ 2x1 -3x2 4x1 + 5x2 {-{lx1 + 3x2 16 -5x1 - 1x2 3 =

=

5

A=

7

=

B

=

=

e -:J [-{2 3]

g

[_�]. [163]·

h=

-1

-5

=

Adding the corresponding equations, we obtain a third system of equa­ tions,

{(2 + -{l)x1 + (-3 + 3)x2 (4 + - l)X2 -

and its matrices

c=

5)Xt

(

5

[24--{2 -35-13] +

+

5

=

=

(

5+

(-7 +

-[

k-

16) 3 ),

5+ -7+

16] 3

.

Here we see how C may be obtained directly from A and B without reference to the original system of equations as such. We simply add the entries in like position. Thus

c22 +4 a22 + b22 ++ =

=

and we shall write C = A

B.

We shall define this sum A

Bonly when A and B have the same

number of rows and columns. Two matrices with this property will be called •

(t.2.1)

the suin C

+

DEFINITION =

A

For such matrices we make the following

If A and Bare two

Bis given by

i=

l, · · ·,

m

m, j =

X

n

matrices, then

1, · · ·,

n.

10

THE ALGEBRA OF MATRICES •

(1.2.5) DEFINITION OF SCALAR MULTIPLICATION Let = [a1j] be an m X n matrix with entries in the number system F, and aA = Aa is defined by let a be an element of F. Then B

A

=

i= I,

.

.

.,m,

j= I,

· · ·, n .

Directly from the definition w e have the following •

(1.2.6)

(I(2))

{a+{3)A

(3) (af3)A

(4)

THEOREM

a(A+B)

=

a(f3A).

IA (1) =

=

aA+aB. aA +{JA.

=

A.

To illustrate

A

let a

=

2,

[ -12 OJ 3

=

B

0

=

[-II

Then

-� :J) 2 [I 21 11 J [2 2 22J 2 [ 21 I J 2 [-11 -1 IJI [ -2 2 OJ [-22 -2 22J [2 2 22J. 4

=

aA +aB

0

=

3

=

4

6

0

o + 0

0

0

4

+

0

So in this particular case we have a(A +B)

=

=

0

aA +aB. The proofs

are straightforward and are left to the reader. Although aA

=

Aa for a a scalar and A

a

matrix, if A is a row vector,

we shall always write the product as aA. However, if B is a column vector, the product of B and a will be written as Ba.

11

2.

ADDITION AND SCALAR MULTIPLICATION OF MATRICES

EXERCISES I. None of the following matrices are equal. Check! Which of them are

conformable matrices of the same order. (a)

A�

( c) C

=

[: � -;] 4

[� � � �]. 3

6

(o)

W

E

G�

4

0

( b)

B�

(d) D

-2

=

[� : -;] [; : -;J 4

4

[� � �]. 3

6

7

0

[: -;]

(O

( h)

F

H

4 1

-2

7

[� ; -�]

[� -� �J

2. Add the following pairs of systems of equations by finding their matrices A, b, and x. After performing the matrix addition translate the result back into equation form. Check your result by adding

the equations directly. (a)

(b)

{

{

4x1 + ?x2

=

Xt + 2x2 - X3 4x1 + 2x2 6x1 + 3x2

-x1 + l3x2

=

=

3

=

7

=

8

2 3

{

{

7x2 + 8x3 XI - X2 X1 - 3x2 Xt + X2

4x1 + 2x2

=

=

=

=

=

-2 0

3.

0

17.

12

THE ALGEBRA OF MATRICES

(c)

(d)

r:

{

2x+ y+z=

f

(3 + 2i)z1 + (-2 + 4i)z2 = 2 +i

x- 2y-z=

1 -

x- y+z=

l

2

+

l -2x +

4

y

_,

1

=;

14y -z=

3.

and

(4 + 4i)z1 + (-7 + 7i)z2 = 4 - i

{

3z1 + (4 - 4i)z2 = 6 Z1 + (2 + 2i)z2 = 7 -i.

3. Check parts (3) and (4) of Theorem (1.2.2). 4. Prove part (5) of Theorem (l.2.2). [Hint: In this and exercise 3 use the definition of matrix addition as illustrated in the proof of part (2).] 5. Let C be an X n· matrix. The trace of C, tr(C), is defined to be the sum of the diagonal entries, that is, tr(C) = 2:{=1 Cit. Deduce the following results: (a) tr(A + B) = tr(A) + tr(B).

6.

(a)

{

(b) tr(kA) = k tr(A). (b)

4x1 - 5x2 + 7x3 = 0 2x1 + 6x2 - X3 = I.

{

2x1 - 3x2 + XJ = - X1 + 2x2 - X3 = -4 -3X1

+ 5X3 =

12.

Find the scalar product of the given systems of equations by 3. First perform the multiplications. Find the associated matrices and calculate the scalar product using the definition. Translate the result back into equations. Compare the respective answers.

7.

Check the assertions following the definition of scalar multiplication.

8. For the system of equations

{

iz1 +

2iz2 + (4 - i)z3 = 1

z1 + (2 - i)z2 + (1 + i)z3 = -i,

find the product of the corresponding matrix equation with the scalar ( 1 - i).

3.

MATRIX MULTIPLICATION Suppose we now consider the system of equations

(1.3.1)

{

YI

=

3x1 - 5x2

Y2 = 5x1 + 3x2

14

THE ALGEBRA

MATRICES

OF

so that if the product BA is to be defined we should have

(1.3. 7)

BA= C =

Observe that knowns

[

3+ 5

-5 + 3

3 - lO

-5 - 6 .

0+ 15

0+9

]

(l.3.3) was a system of three equations in the two un­

y1 and y2, and that (l.3.1) had one equation for each of y1 and

y2. Thus in our substitution the number of unknowns in

(l.3.3) equals

(l.3. l).

the number of equations in

In terms of matrices this means that the number of columns of B

equals the number of rows of A. Further, after the substitution has been

(l.3.6), it is clear that the number of equations equals (l.3.3), while the number of unknowns is the same as in (l.3.1). Thus our new matrix BA will have the same carried out in

the number of equations in

number of rows as B and the same number of columns as A. With this in mind we shall call an matrix A

m

X

n

matrix B and an

multiplicatively conformable if and only if

n

=

' n ,

n

'

X p

that is, if

the number of columns of B equals the number of rows of A. We shall define multiplication only for multiplicatively conformable matrices. Further, BA will be an

m

X

p matrix; that is, BA will have as many

rows as B and as many columns as A. Keeping

• n

(l.3.7) and subsequent remarks in mind we make the following

(1.3.8) DEFINITION

Let B be an

m

X

n

]

matrix and A be an

X p matrix, so that B has as many columns as A has rows. Let

B=

[�11

Then the product

.

bml

�In] .

A=

.

bmn

BA= C =

[Ctt :

Cmt

[�11 .

.

a.1

"']

Cm p

�Ip .

.

anp

.

16

THE ALGEBRA OF MATRICES

We can immediately verify the following special cases: (1) row vector X matrix row vector. =

(2) matrix X column vector

=

column vector.

(3) row vector X column vector

=

( 4) column vector X row vector

=

(t.3.12)

EXAMPLE

l X l matrix (a scalar). matrix.

Let

and C= BA. Then

C=

[

l·O+l·l+2·2

l·l+l·(-1)+2·0

1·0+2·1+3·2

l·l+2 (-1)+3 . 0

l·O+ 4·1+9·2

l·l+ 4 (-1)+9·0

] [5 �] =

8

22

-

·

-3

We remark that Ccan be obtained from Band A by a row into column multiplication. Thus

c11 is the sum of the entrywise products going across

the first row of Band down the first column of A, so in (l.3.12)

c11

=

1·0+l·l+2·2 =

5

b1.a.1,

=

a•1 is the first column of A. Similarly, c32 can be obtained by going across the third row of B and down the

where b1• is the first row of Band

second column of A. Again, in (l.3.12) we have

c32

=

1·l+ 4(-l)+9·0

=

-3

=

b3.a.2.

In general

(1.3.13)

CtJ

=

(b11

b;2

b,.]

The formula (1.3.13) holds not just in this one example but whenever the product C

=

BA is defined.

18

THE ALGEBRA OF MATRICES •

(1.3. t 7)

THEOREM

C =BA, then the jth column of C is a B with coefficients from the jth

If

linear combination of the columns of column of

A.

The reader should also check that in Example

(l.3.12),

and in general n

C =b.1a1• + Note that for each k,

·

·

·

b.kak• is an

+b••a •• m

=

I: b.kak•· k�J

X p matrix.

Let us now return to some properties of matrix multiplication. We can now prove

(1.3.18)

THEOREM

For all matrices

A, B, and C, and any

scalar a:

(l) A(BC) =(AB)C. (2) A(B+C) =AB+AC. (3) (A+B)C =AC+BC. (4) a(AB) =(aA)B. whenever all the products are defined. PROOF OF

(1)

We must show that

AB =D, BC = G, (AB)C =F, and A(BC) =H. F = H.

Let

Hence

The proofs of as an exercise.

(2), (3), and (4) are similar and are left to the reader

20

THE ALGEBRA OF MATRICES

EXERCISES

I. Check the following products.

a

(

)

[�l -=�3 ][� -:J [-4� -=:4] =

{2 -�] [O) 0. 2{-� :J [2 [; -�I-:J [ _:J .2 l 2 3 . (Hint: 3. (2 ) (3) 4. 1A 2 0 2 O �[ �][� -�J. [ 0 -2 ][ 0 lJ : 0 (b) (c)

(d)

[l [O

=

=

=

8).

=

Which of the matrices in exercise

of Section

conformable? Calculate three such products. Check parts

tion.

and

of Theorem ( 1 .

) Compute the following products:

(a)

.

18 )

are multiplicatively Use the defini­

(b)

[ -�r� -:J (dT� -� -m 0 J ( a,. and a,.

-->

a,. (read row a,. becomes row a,. and row a,.

becomes row a,.). Type JI

The multiplication of any row by a nonzero scalar >..,

denoted symbolically by a,.--> >..a,•.

50

LINEAR EQUATIONS In the general case a matrix of type II is of the form

En =

I

0

0

0

0

l

0

0

0

O

X

0 '

0

0

·

·

·

where X � 0, and in the special example

m

0

0

X

0

0

l

0

0

= 4,

r

=

2,

In the ge_neral situation a matrix of type III is of the form

Em=

l

0

0

0

0

l

0

0

0

0

0

0

l

·

·

·

X

·

·

·

O

'

where X occurs in the rth row of the sth column. Again for s

=

3, we have

m =

4,

r =

2,

52

LINEAR EQUATIONS •

E1-1

(2.2.1) £1,

LEMMA

Ei, Eu,

and

Eu1

are nonsingular.

In

fact,

=

I

0

0

0

I

0

1/>.

0

0

1

0

0

0

0

1

0

0

0

0

O

0

0

0

· ·

·

I

· · ·-

>.

· · ·

O

Thus the inverse of an elementary matrix of type I, II, or III is again an elementary matrix of the same type.

PROOF

The proof is by direct computation and is left to the reader.

£111-1 is £111£111-1

the matrix defined above, then the reader should check that

If

=

Remark

I=

E111-1E111.

Lemma

(2.2. l)

can easily be interpreted in terms of ele­

mentary row operations. For example, since

E1-1 =E1,

E1(E1A) =A. This asserts that if we interchange two rows of

A

change the same two rows of B, we again obtain

to obtain B, then inter­

A.

54

LINEAR EQUATIONS

(2.2.3) LEMMA Let A, B, and C denote (I) For all A, A�A. (2) If A � B, then B� A. (3) If A � B, and B� C, then A � C.

m

X

n

matrices. Then

PROOF

(I) A = IA, and since I is an elementary matrix, part {I) follows. (2) Let A� B. Then for suitable £1, ·· ·, E, we have B =PA, where P = E,E,_1 ···£1. It follows from Lemma (l.6.5) that P is nonsingular and p-1 = E1-tE2-1. · ·E,-1. As already noted, the inverse of each elemen­ tary matrix is an elementary matrix. Thus A = P-IB and B �A . (3) Let A�B,B � C. B =PA and C = QB, where P E,· ··E1 and Q = E,· ··E,tt. Hence QP = E,· ··E1 and C = QPA. Thus A � C. =

A and B are row equivalent rather A is row equivalent to B. Using Lemma (2.2.3) we can now prove a

In view of this lemma we may say than

(2.2.4)

THEOREM

Let

A �B, say

B

=

PA, P a product of ele­

mentary matrices. Then the systems of equations Ax = b and PAx

=

Pb

are equivalent. [Recall Definition (2.1.5) of equivalent systems of equa­ tions.] PROOF

Let

c be a solution of Ax = b; that is, Ac = b. Obviously,

PAc =Pb. Conversely, suppose PAc =Pb. P is nonsingular [see the proof of Lemma (2.2.3)]. Therefore, or

Ac= b.

EXERCISES

I. (a) Suppose

A = [a;1] is an

m

X

n

matrix, and suppose that B1, B2,

A by performing the elementary row operations a, ......,. a,. and a, ......,. a,., a, ......,. Xa,., and a, ......,. a,. + Xa,., respectively. Show that there are elementary matrices Er,

83 are each obtained from •

Err, and Errr of types I, II, and III, respectively, such that Bi = ErA, B2 = ErrA, and 83 = ErrrA.

(b) Conversely, suppose C1 = ErA, C2 = ErrA, and C3 = ErIIA, where Er, En, and Errr are elementary matrices of types I, H, and III, respectively. Show that C1, C2, and C3 can each be obtained from

A by an elementary row operation of types

II, and III, respectively.

I,

56

LINEAR EQUATIONS 5. Let

D

Ut E,

[: � �] [-� : ;] [: � �] [-: : ;] [: � �] •nd

E,

D'

E,

E1D and I= (E3E2)D'. Does DE1 = I= D( ' E3E2)? D' nonsingular? Let D be an n X n diagonal matrix. Show that Dis nonsingular if and d .= 0. (Hint: If d11d22 d .= 0, can du 0? only if d11d22 and verify that I=

Are D and 6.

·

·

·

••

·

·

·

=

••

What is n-17 Write n-1 as the product of elementary matrices.) 7. Prove that elementary matrices of type II commute. Find two elemen­

tary matrices of type I that do not commute; find two elementary matrices of type III that do not commute. 8. Let

A=[� ; 1�] 0

Show

that

AT� BT (�

-

12

B

=

[� ; �]· 0

-6

A � B. (Hint:

Recall exercise

called the transpose of

9.

and

A

0

0

2.) Show also that The matrix

AT is

and is obtained by the operation.

(a) Obtain

by premultiplying I by a sequence of four elementary matrices, E4,

£3, E2, and £1, where £4 is of type

II and the others are of

type III. (b) Generalize (a): Show that any type I is a product are of type III.

m

X

m

elementary matrix of

£4£3£2£1, where £4 is of type

II and the others

57

3.

ROW ECHELON FORM

10. Prove the following. (a) If A � I, then A-1 � I (assume A-1 exists). (b) If A �/,then AT�/. 11. Prove or disprove (with a counterexample) this statement: If A and B are multiplicatively conformable matrices in row echelon form, then AB is in row echelon form.

3.

ROW ECHELON FORM We have seen one example of a matrix in row echelon form, the

coefficient matrix in equation

[�

(2.1.9). Another is

� -� �

0

0

0

0

0

0 0

We shall now show that any

0

�] I

.

0

m X n matrix is equivalent to a matrix in

row echelon form. The proof will be accomplished by actually exhibiting a step-by-step process,using row operations,that systematically reduces

A to the prescribed form. Such a step-by-step process is called an algorithm. (This particular algorithm is called Gaussian elimination.) Roughly

speaking, we could program this process on a computer in such a way that,given the matrix

A, the computer would print out the row echelon

form of A. Those readers who have some experience with computers will

recognize the following as an informal flow chart for the algorithm.

For convenience,the following abbreviations will be used in the chart:

REF,row echelon form; and ero, elementary row operation.

For readers who have not seen a flow chart before, we may briefly

explain that the flow chart describes how the machine repeats a sequence of operations. We shall call each sequence a

step. Initially k

end of the step the machine reaches the instruction "put k:

and this will mean that in step 2, k

=

=

=

I. At the k + I,"

2, and so on. Observe also that

several instructions change the matrices Ak and A. Using a convention

observed by computers, we again call the new matrices so obtained Ak

and A. We shall use the same convention in the formal proof below. We now give an example to illustrate the algorithm. Let

0

3

-I

4

-I

7

-] I

7 . 6

58

LINEAR EQUATIONS

START

NO

Find first nonzero column of Ak, soy column p.

Find first row of Ak hoving nonzero entry in column p.

Put this row first in Ak (ero of type I).

Make leoding entry of first row of Ak equol to 1 (ero of type 11).

Reduce oll other entries of column p of

A to zero by subtrocting oppro­

priote multiples of first row of Ak from other rows of A (ero of type Ill).

YES

NO

Partition A,

(A is in REF) STOP

where B hos

k rows.

Put k:

k

+

1.

59

3.

ROW ECHELON FORM

Step

1

(c) Does

(a) and (b) We let k = I and put

At = O?

(d) The first nonzero column of (e) The first row of

(f)

Ai

[

Ai

p = 2.

is

having nonzero entry in column

Perform the operation

ai•--+ ai•

O

A=

0

0

0

-l

(h) Perform the operation

a1•--+ a1•

(i)

Does I = Partition

3? A,

No.

[� [[:

+

0

3

0

3

-4

0

3

0

3

where

and we loop back to (c) to begin step

Step

2

(c) Does

Ai = 0?

2.

so that

ai•.

-4

[O

A=

is row

l

-

ai•--+ -ai•,

A�

2

and ai•--+ a1 . . We now have

(g) Perform the operation

(j)

Ai = A.

No.

Then

-]

-l .

-17

=:].

2.

No.

(d) The first nonzero column of

Ai

is

p = 3.

(e) The first row of Ai having nonzero entry in column (f) This operation is unnecessary.

3

is row I.

60

LINEAR EQUATIONS

[[� �

(g) Perform the operation

Ot•--+

[O

A=

% a1.,

1

-4

3

a1•--+ a1•

(0

A=

(i)

Does 2 =

(j)

Partition

3? A,

Ak+1= A 3 3,

Step 3

(d) Stop.

03• --+ a3. -

[O

0

0

0

1

(0

0

0

-�

3 02•.

,

OJ.

0

and we loop back to (c) to begin step

(c) Does

A

402• and

1

=

(k) Now k =

+

.

-1

[[O O _2%J] OJ

No.

A=

where

-7]

- }�

[[�

(h) Perform the operations Thus

so that

A 3 = O?

3.

Yes.

is in row echelon form.

We shall now state the theorem formally and give a proof based on the algorithm.

(2.3.1)

Any

THEOREM

m

X

n

matrix

A

is row equivalent to a

matrix in row echelon form. PROOF Step 1

A F

If A = 0, then

0. Suppose

O•p

A

is in row echelon form. So we may assume

is the first nonzero column of

A

(often p=

1)

and

that a1p is the first nonzero element in the column. The operation a1.--+ a1• and

a1•--+ a;•.

1, this operation of type I is unnecessary. a1•--+ (a1p)-1a1•. Finally, calling the new the operations a;•--+ a;. - ( a1p) a1•, for i F I, I, a;p 0 for matrix A we observe that a1p

Again, if i

=

Next, perform the operation first row 01., perform I < i 5,m. In the new

i F 1,

and

/(1)=

p. Pa rtiti on A,

=

=

62

LINEAR EQUATIONS

and note that

(2) the first l(k) columns of Ak+1 are zero. Thus the conditions for starting the next step are satisfied. We carry out

r

steps of this algorithm until either A,+1

=

0 or

r = m.

The resulting matrix, which will now be denoted by AR, is in row echelon form and is row equivalent to the original matrix.

A square matrix in row echelon form is obviously in upper

Remark

l.3 for the definition). Thus the (2.3.l) gives a method for reducing every matrix to upper

triangular form (see exercise 6 of Section algorithm of

triangular form by elementary row operations. However, if it is merely desired to reduce A to upper triangular form, not necessarily to row echelon form, then several steps of the algorithm we have given may be omitted (see exercise 5 of this section).

(2.3.2)

CAUTION

Theorem

(2.3.l) shows that for each matrix A � AR. In Chap­

there exists a matrix AR in row echelon form such that A ter

3 we shall show that AR is unique; that is, there is just one matrix in

row echelon form row equivalent to A. Thus we shall be entitled to call AR the row echelon form of A. We shall use this terminology in the rest of this chapter, although the uniqueness of AR has not been proved. A reader concerned with logical precision should read Chapter 3

through Section 6 before continuing with the rest of this chapter. How­ ever, we recommend this course only for those readers with previous experience in linear algebra. Chapter

3 i& rather abstract, and the reader

will be aided in understanding it by a familiarity with the solution of linear equations, which we shall discuss in Section 4.

EXERCISES

]

I. Following the algorithm reduce the following matrices to row echelon

form:

(•l

(c)

-

8

-3

h �]

16 6 l

(b) .

(d)

[�

[;

2

5

-2

2 0

2

0

-3

:J

:J

64

LINEAR EQUATIONS • (2.4.t) DEFINITION The system of equations Ax= b is homo­ geneous if and only if b = 0. We-then write Ax= 0. Let Ax = 0 be a homogeneous system of linear equations, and let AR be the row echelon form of A, which exists by Theorem (2.3.1). By Theorem (2.2.4) the system of equations ;4.Rx= 0 is equivalent to Ax= 0. Consequently we may assume from the outset that A itself is in row echelon form. It is clear that the system Ax = x=

0.

We call this solution the

0 always has at least trivial solution, and it is

one solution, usually of no

interest. Before considering the general case, let us inspect an example:

Ax�

(2.4.2)

[�

x.co

0

0

2

0

0

0

0

0

0

0

0

0

0

-�]

�/(I) Xz(2J

=

X/(2)

0.

X/(3) Xz(3)

If we write out the corresponding equations, we can separate the x10, from the Xz· By putting the x1ui on the left and the Xzci> on the right in the equations, and transforming to matrix vector form, we obtain

[::: :] -c[::: :] -[: � =�][::: :]. =

(2.4.3)

X/(3)

It is easily checked that

(2.4.3)

=

Xz(3)

(2.4.2)

0

and

(2.4.3)

call

[] X/(1)

X/(2)

X/(3)

the I vector and

[] Xz(I)

Xz(2) Xz(3J

0

7

Xz(JJ

yield the same solutions.

In

66

LINEAR EQUATIONS Let Ax

(2.4.6) RESULT

=

0 be a homogeneous system of m linear

equation in n unknowns and suppose that A is in row echelon form with

t

r

n

=

nonzero rows. Then the general solution of the system Ax -

of the

z

r

=

0 has

arbitrary parameters that may be chosen to be the components

vector. The elements of the solution are linear expressions in

the parameters. To restate our results for mafrices A that need not be in row echelon form we need a LetA be a m X n matrixand

(2.4.7) TEMPORARY DEFINITION

let AR be the row echelon form of A. The

rank r

of A is the number of

nonzero rows of AR. We remark in passing that

r

min(m, n). This fact will be useful later.

The logically precise will have observed that this definition uses the uniqueness of the row echelon form of A. Uniqueness will be proved in Chapter

3, and this definition will be replaced by a better one. (2.3. l) every system of equations Ax = 0, where A need not be in echelon form, is equivalent to the system ARX = 0, where AR is the

By row

row echelon form of A. Hence with this definition we are in a position to state the result (we do not call it a theorem, as a more precise version will appear in Chapter

Let Ax

(2.4.8) RESULT

equations in

n

3). =

0 be a system of m homogeneous linear

unknowns. If the rank of A is

r

and t

=

n

-

r,

then the

general solution of the system has t arbitrary parameters. The elements of the solution are linear expressions in the parameters. We shall now elucidate the last sentence of the result that for each choice of

z

(2.4.6).

We know

vector, there is but one solution of our homo­

geneous system of equations. Suppose we choose

x,

=

'Y• and apply the corollary, it will follow that a solution is S

=

sl'YI

+

·

·

·

+

S1'"(1.

This solution is the general solution by virtue of (2.4.6). For the sake of reference, we shall call the solutions si, • • · , s1 a set of basic solutions of the system Ax

=

0.

We can sum this up in the following

(2.4.11)

tions in t

=

11

n

Let Ax

RESULT

be

0

=

set of

a

m

homogeneous equa­

unknowns. Let the rank of A be r; let I s1,

·,

stj, where

- r, be the set of basic solutions; and let 'Yt. · • ·, 'Y1 be scalars.

Then the solution set S consists of all vectors S =

S1'Yt

+

·

'

·

+

s

of the form

S1'Yt•

Consider the system of equations

{ [�

Ax

Then

A,�

Consequently, /(1)

=

-3

llm

0

-3 -3

-3

-]

0

0 0

0

I, 1(2) = 3, z(l ) = 2, and z(2)

system in the form of (2.4.5) we have

[ [ J [ C=

X/(l)

Xtc2>

=

_

-3

2

0

-1

-3

2

0

-1

] ][ J Xz(l) Xz(2)

·

=

4. Writing this

70

LINEAR EQUATIONS

The general solution is

If we take 'Y 1

1, 'Y2

=

-2 we obtain a particular solution

[J

x•�

while if we take 'YI

=

2 and

'Y2

-3, we obtain a particular solution

=

It is not hard to prove that the general solution of our equations can be written in the form

For instance,

s• = x'(-3) + x2(2) and

We next treat the special case m

r :;: n.

;;:: n for this situation to exist.

s2

=

Since

r

x'(-2) + x2(1). � min(m, n) we must havt