Lecture 1

Systems of Linear Equations

Today- Course Overview- Introduction to Systems of Linear Equations- Matrices, Gaussian Elimination and Gauss-Jordan EliminationReading Assignment: Chapter 1 of TextHomework #1 Assigned (Due 10/05)

Elementary Linear AlgebraR. Larsen et al. (5 Edition) TKUEE 翁慶昌 -NTUEE SCC_09_2007

Lecture 1

Systems of Linear Equations

Next Time

- Operation with Matirces

- Properties of Matrix Operations

- Inverse of a Matrix

Reading Assignment: Secs 2.1-2.3 of Textbook

Elementary Linear AlgebraR. Larsen et al. (5 Edition) 翁慶昌 -SCC_09_2007

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Purchasing Mix Problem

Mr. Wang has NT$1,000,000 and he wants spends all the money to buy 100 animals

Price tags:

NT$ 5000/sheep

NT$ 10,000/cow

NT$100,000/horse

Q: Formulate the problem mathematically?

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Circuit Analysis

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Examples from Your Daily Life ?

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Algebra and Linearity

Algebra

Real Numbers or Sets

Binary Operations

Identity elements

Inverse elements

Associativity

Commutativity Linearity of a function

Additivity f(x+y) = f(x)+f(y)

Homogeneity f(ax) = af(x)

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1.0.1 History of Linear Algebra

http://en.wikipedia.org/wiki/Linear_Algebra#History A Brief History of Linear Algebra and Matrix Theory.htm nla-1.ppt

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1.0.2 Course Overview (Syllabus)

LA_4_mgmt_Syllabus_2007.doc

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1.1 Introduction to Systems of Linear Equations

a linear equation in n variables:

a1,a2,a3,…,an, b: real number

a1: leading coefficient

x1: leading variable Notes:

(1) Linear equations have no products or roots of variables and

no variables involved in trigonometric, exponential, or

logarithmic functions.

(2) Variables appear only to the first power.

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Ex 1: (Linear or Nonlinear)

723 )( yxa 2 21

)( zyxb

0102 )( 4321 xxxxc 221 4)

2sin( )( exxd

2 )( zxye 42 )( yef x

032sin )( 321 xxxg 411

)( yx

h

powerfirst not the

lExponentia

functions rictrigonomet

Linear

Linear Linear

Linear

Nonlinear

Nonlinear

Nonlinear

Nonlinear

powerfirst not the

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a solution of a linear equation in n variables:

Solution set:

the set of all solutions of a linear equation

bxaxaxaxa nn 332211

,11 sx ,22 sx ,33 sx , nn sx

bsasasasa nn 332211s.t.

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Ex 2 ： (Parametric representation of a solution set)

42 21 xx

If you solve for x1 in terms of x2, you obtain

By letting you can represent the solution set as

And the solutions are or Rttt |) ,24(

,24 21 xx

tx 2

241 tx

Rsss |)2 ,( 21

a solution: (2, 1), i.e. 1,2 21 xx

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a system of m linear equations in n variables:

mnmnmmm

n

nn

nn

bxaxaxaxa

bxaxaxaxabxaxaxaxabxaxaxaxa

332211

333333232131

22323222121

11313212111

Consistent:

A system of linear equations has at least one solution. Inconsistent:

A system of linear equations has no solution.

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Notes:

Every system of linear equations has either

(1) exactly one solution,

(2) infinitely many solutions, or

(3) no solution.

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Ex 4: (Solution of a system of linear equations)

(1)

(2)

(3)

13

yxyx

6223

yxyx

13

yxyx

solution oneexactly

number inifinite

solution no

lines ngintersecti two

lines coincident two

lines parallel two

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Ex 5: (Using back substitution to solve a system in row echelon form)

(2)(1)

252

yyx

Sol: By substituting into (1), you obtain2y

15)2(2

xx

The system has exactly one solution: 2 ,1 yx

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Ex 6: (Using back substitution to solve a system in row echelon form)

(3)(2)(1)

253932

zzyzyx

Sol: Substitute into (2) 2z

15)2(3

yy

and substitute and into (1)1y 2z

19)2(3)1(2

xx

The system has exactly one solution:2 ,1 ,1 zyx

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Equivalent:

Two systems of linear equations are called equivalent

if they have precisely the same solution set.

Notes:

Each of the following operations on a system of linear

equations produces an equivalent system.

(1) Interchange two equations.

(2) Multiply an equation by a nonzero constant.

(3) Add a multiple of an equation to another equation.

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Ex 7: Solve a system of linear equations (consistent system)

(3)(2)(1)

17552

43932

zyxyx

zyx

Sol:

(4) 17552

53932

(2)(2)(1)

zyxzyzyx

(5)

153932

(3)(3)2)((1)

zyzyzyx

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So the solution is (only one solution)2 ,1 ,1 zyx

(6)

4253932

(5)(5)(4)

zzyzyx

253932

)6((6) 21

zzyzyx

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Ex 8: Solve a system of linear equations (inconsistent system)

(3)(2)(1)

13222213

321

321

321

xxxxxxxxx

Sol:

)5()4(

24504513

(3)(3))1((1)

(2)(2)2)((1)

32

32

321

xxxxxxx

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So the system has no solution (an inconsistent system).

2004513

)5()5()1()4(

32

321

xxxxx

statement) false (a

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Ex 9: Solve a system of linear equations (infinitely many solutions)

(3)(2)(1)

13130

21

31

32

xxxxxx

Sol:

(3)(2)(1)

13013

)2()1(

21

32

31

xxxxxx

(4)

033013

(3)(3)(1)

32

32

31

xxxxxx

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013

32

21

xxxx

then

,

,

,13

3

2

1

tx

Rttx

tx

tx 3let

,32 xx 31 31 xx

So this system has infinitely many solutions.

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Keywords in Section 1.1:

linear equation: 線性方程式 system of linear equations: 線性方程式系統 leading coefficient: 領先係數 leading variable: 領先變數 solution: 解 solution set: 解集合 parametric representation: 參數化表示 consistent: 一致性 ( 有解 ) inconsistent: 非一致性 ( 無解、矛盾 ) equivalent: 等價

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(4) For a square matrix, the entries a11, a22, …, ann are called

the main diagonal entries.

1.2 Gaussian Elimination and Gauss-Jordan Elimination

mn matrix:

mnmmm

n

n

n

aaaa

aaaaaaaaaaaa

321

3333231

2232221

1131211

rows m

columns n

(3) If , then the matrix is called square of order n.nm

Notes:

(1) Every entry aij in a matrix is a number.(2) A matrix with m rows and n columns is said to be of size mn

.

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Ex 1: Matrix Size

]2[

0000

21

031

4722

e

11

22

41

23

Note:

One very common use of matrices is to represent a system

of linear equations.

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a system of m equations in n variables:

mnmnmmm

nn

nn

nn

bxaxaxaxa

bxaxaxaxabxaxaxaxabxaxaxaxa

332211

33333232131

22323222121

11312212111

mnmmm

n

n

n

aaaa

aaaaaaaaaaaa

A

321

3333231

2232221

1131211

mb

bb

b2

1

nx

xx

x2

1

bAx Matrix form:

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Augmented matrix:

][ 3

2

1

321

3333231

2232221

1131211

bA

b

bbb

aaaa

aaaaaaaaaaaa

mmnmmm

n

n

n

A

aaaa

aaaaaaaaaaaa

mnmmm

n

n

n

321

3333231

2232221

1131211

Coefficient matrix:

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Elementary row operation:

jiij RRr :(1) Interchange two rows.

iik

i RRkr )(:)( (2) Multiply a row by a nonzero constant.

jjik

ij RRRkr )(:)((3) Add a multiple of a row to another row.

Row equivalent:

Two matrices are said to be row equivalent if one can be obtained

from the other by a finite sequence of elementary row operation.

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Ex 2: (Elementary row operation)

143243103021

143230214310

12r

212503311321

212503312642 )(

121

r

8133012303421

251212303421 )2(

13r

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Row-echelon form: (1, 2, 3)

(1) All row consisting entirely of zeros occur at the bottom

of the matrix.

(2) For each row that does not consist entirely of zeros,

the first nonzero entry is 1 (called a leading 1).

(3) For two successive nonzero rows, the leading 1 in the higher

row is farther to the left than the leading 1 in the lower row.

Reduced row-echelon form: (1, 2, 3, 4)

(4) Every column that contains a leading 1 has zeros everywhere

else.

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form)echelon

-row (reduced

form)

echelon -(row

Ex 4: (Row-echelon form or reduced row-echelon form)

210030104121

10000410002310031251

000031005010

0000310020101001

310011204321

421000002121

form)

echelon -(rowform)echelon

-row (reduced

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Gaussian elimination:

The procedure for reducing a matrix to a row-echelon form.

Gauss-Jordan elimination:

The procedure for reducing a matrix to a reduced row-echelon

form.

Notes:

(1) Every matrix has an unique reduced row echelon

form. (2) A row-echelon form of a given matrix is not unique.

(Different sequences of row operations can produce

different row-echelon forms.)

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The first nonzerocolumn

Produce leading 1

Zeros elements below leading 1

leading 1

Produce leading 1

The first nonzero column

Ex: (Procedure of Gaussian elimination and Gauss-Jordan elimination)

1565422812610421270200

1565421270200281261042

12r

15654212702001463521

)(1

21

r

2917050012702001463521)2(

13r

Submatrix

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Zeros elements below leading 1

Zeros elsewhere

leading 1

Produceleading 1

leading 1

1 2 5 3 6 14

0 0 1 0 7 2 6

0 0 5 0 17 29

)(2

21r

12100006270100

1463521)5(23

r

2100006270100

1463521)2(3r

210000100100703021)6(

31r )(

3227

r )5(21r

Submatrix

form)echelon -(rowform)echelon

-row (reduced

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Ex 7: Solve a system by Gauss-Jordan elimination method (only one solution)

1755243932

zyxyx

zyx

Sol:matrix augmented

1755240319321

111053109321)2(

131

12 , rr

2100531093211

23r

210010101001)9(

31)3(

322

21 , , rrr

211

zy

x

form)echelon -(row

form)echelon -row (reduced

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Ex 8： Solve a system by Gauss-Jordan elimination method (infinitely many solutions)

1 530242

21

311

xxxxx

13102501

)2(21

)1(2

)3(12

)(1 ,,,2

1 rrrr

is equations of system ingcorrespond the

13 25

32

31

xxxx

3

21

variablefree

, variableleading

x

xx

：：

Sol:

10530242

matrix augmented

form)echelon

-row (reduced

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32

31

3152

xxxx

Let tx 3

,

,31

,52

3

2

1

tx

Rttx

tx

So this system has infinitely many solutions.

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Homogeneous systems of linear equations:

A system of linear equations is said to be homogeneous

if all the constant terms are zero.

0

0 0 0

332211

3333232131

2323222121

1313212111

nmnmmm

nn

nn

nn

xaxaxaxa

xaxaxaxaxaxaxaxaxaxaxaxa

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Trivial solution:

Nontrivial solution:

other solutions

0321 nxxxx

Notes:

(1) Every homogeneous system of linear equations is consistent.

(2) If the homogenous system has fewer equations than variables,

then it must have an infinite number of solutions. (3) For a homogeneous system, exactly one of the following is true.

(a) The system has only the trivial solution.

(b) The system has infinitely many nontrivial solutions in

addition to the nontrivial solution.

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Ex 9: Solve the following homogeneous system

020

321

321

xxxxxx

01100201

)1(21

)(2

)2(12 ,, 3

1

rrr

Let tx 3

Rttxtxtx , , ,2 321

solution) (trivial 0,0When 321 xxxt

Sol:

03120311

matrix augmented

form)echelon

-row (reduced

3

21

variablefree

, variableleading

x

xx

：：

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Keywords in Section 1.2:

matrix: 矩陣 row: 列 column: 行 entry: 元素 size: 大小 square matrix: 方陣 order: 階 main diagonal: 主對角線 augmented matrix: 增廣矩陣 coefficient matrix: 係數矩陣

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elementary row operation: 基本列運算 row equivalent: 列等價 row-echelon form: 列梯形形式 reduced row-echelon form: 列簡梯形形式 leading 1: 領先 1 Gaussian elimination: 高斯消去法 Gauss-Jordan elimination: 高斯 - 喬登消去法 free variable: 自由變數 homogeneous system: 齊次系統 trivial solution: 顯然解 nontrivial solution: 非顯然解