chapter 2 basic linear algebra ( 基本線性代數 ) to accompany operations research: applications...
TRANSCRIPT
Chapter 2
Basic Linear Algebra( 基本線性代數 )
to accompany
Operations Research: Applications and Algorithms
4th edition
by Wayne L. Winston
Copyright (c) 2004 Brooks/Cole, a division of Thomson Learning, Inc.
2
§2.1§2.1 – Matrices( 矩陣 ) & Vectors( 向量 )
A matrix is any rectangular array of numbers
If a matrix If a matrix AA has has mm rows and rows and nn columns it is columns it is referred to as an referred to as an mm x x nn matrix. matrix.
mm x x nn is the is the orderorder( 階 ) of the matrix. It is of the matrix. It is typically written astypically written as
1
3
2
4
1
4
2
5
3
6
1
2
2 1( )
A
a 11
a 21
....
a m1
a 12
a 22
....
a m2
....
....
....
....
a 1n
a 2n
....
a mn
3
The number in the The number in the iith row and th row and jjth column of A th column of A
is called the is called the ijijthth element element of of AA and is written and is written aaij..
Two matrices A = [aij] and B = [bij] are equal if and only if A and B are the same order and for all i and j, aij = bij.
If A1
3
2
4
and Bx
w
y
z
A = B if and only if x = 1, y = 2, w = 3, and z = 4A = B if and only if x = 1, y = 2, w = 3, and z = 4
4
Any matrix with only one column is a column
vector( 行向量 ) or column matrix (行矩陣 ). The number of rows in a column vector is the dimension of the column vector.
C=
RRmm will denote the set all m-dimensional column will denote the set all m-dimensional column vectorsvectors
Any matrix with only one row (a 1 x Any matrix with only one row (a 1 x nn matrix) is a matrix) is a row vectorrow vector ( 列向量 ) or rowrow matrix ( 列矩陣 ). . The dimension of a row vector is the number of The dimension of a row vector is the number of columns. R=columns. R=
1
2
1 2 3( )
5
Any m-dimensional vector (either row or
column) in which all the elements equal zero is called a zero vector (零向量 )or zero matrix (零矩陣 ) (written 0).
Any Any mm-dimensional vector corresponds to a -dimensional vector corresponds to a directed line segment in the directed line segment in the mm-dimensional -dimensional plane.plane. For example, the two-dimensional vector For example, the two-dimensional vector uu
corresponds to the line segment joining the point corresponds to the line segment joining the point (0,0) to the point (1,2)(0,0) to the point (1,2)
0 0 0( )0
0
6
Other Forms
Diagonal matrix ( 對角線矩陣 )
Identity matrix (單位矩陣 )
Upper triangular matrix(上三角矩陣 )
Lower triangular matrix (下三角矩陣 )
),1,(,][ njijioaaA ijnnij
njiji
jiaaA ijnnij
,1;,0
,1,][
),1,(,][ njijioaaA ijnnij
),1,(,][ njijioaaA ijnnij
njiji
jiaaA ijnnij
,1;,0
,1,][
),1,(,][ njijioaaA ijnnij ),1,(,][ njijioaaA ijnnij
7
Example :
8
Transpose Matrix ( 轉置矩陣 ) P.15
,A nnaij
. a
nj1 m,i1aa ,a
mnTij
ijTijnmij
atrixranspose mcalled a tis][A
then)(][AIfT
9
Example :
34
10
17
41
A TA
3114
4071
10
轉置矩陣之性質
786
921
543
A
708060
902010
504030
B
p.20 #4
11
Square Matrix of Order n( 方陣之乘冪 )
12
31
42A
51
23B
1955
460160
238
167
55
200
2890
432100
170
1610
22
2
2
BA
but
)AB(
Example : (AB)2≠A2B2 (AB = BA)?
13
Symmetric Matrix ( 對稱矩陣 ) & Skew-symmetric Matrix ( 斜對稱矩陣 )
14
Example :
029
205
950
653
542
321
B
A is a symmetric matrix
is a skew-symmetric matrix
15
對稱矩陣與斜對稱矩陣之性質
16
The directed line segments (vectors The directed line segments (vectors uu, , vv, , ww) ) are shown.are shown.
u1
2
v1
3
w1
2
X1
X2 (1, 2)
(1, -3)
(-1, -2)
(p.12-13)
17
矩陣之基本運算 (p.13)
The scalar product( 純量積 ) is the result of multiplying two vectors where one vector is a column vector and the other is a row vector. For the scalar product to be defined, the dimensions
of both vectors must be the same.
The scalar product of u and v is written:
u v u 1 v1 u 2 v2 .... u n vn
u 1 2 3( ) v
2
1
2
u v 1 2( ) 2 1( ) 3 2( ) 10
18
The Scalar Multiple of a MatrixThe Scalar Multiple of a Matrix
Given any matrix Given any matrix AA and any number and any number cc, the matrix , the matrix cAcA is obtained from the matrix is obtained from the matrix AA by multiplying each by multiplying each element of element of AA by by cc..
AdditionAddition of Two Matricesof Two Matrices
Let A = [aij] and B =[bij] be two matrixes with the same order. Then the matrix C = A + B is defined to be the m x n matrix whose ijth element is aij + bij.
Thus, to obtain the sum of two matrixes A and B, we add the corresponding elements of A and B.
A1
1
2
0
3 A3
3
6
0
A1
0
2
1
3
1
B1
2
2
1
3
1
C1 1
0 2
2 2
1 1
3 3
1 1
0
2
0
0
0
0
(p.14)
19
Definition : 矩陣相加
6753
4812
7543
A
3276
8593
4257
B ?C
20
This rule for matrix addition may be used to
add vectors of the same dimension.
Vectors may be added using the parallelogram law or by using matrix addition.
v 2 1( )
u 1 2( )
u v 3 3( )
X1
X2
u
v
u+v
1 2 3
1
2
3
(1,2)
(2,1)
(3,3)
21
Line segments can be defined using scalar
multiplication and the addition of matrices.
If u=(1,2) and v=(2,1), the line segment joining u and v (called uv) is the set of all points in the m-dimensional plane corresponding to the vectorscu +(1-c)v, where 0 ≤ c ≤ 1.
X1
X2
u
v
1 2
1
2c=1
c=1/2
c=0
22
矩陣加法之性質
23
常數乘以矩陣之性質
24
Matrix Multiplication ( 矩陣相乘 ) (p.16)
Given to matrices A and B, the matrix product of A and B (written AB) is defined if and only if the number of columns in A = the number of rows in B.
The matrix product C = AB of A and B is the m x n matrix C whose ijth element is determined as follows: ijth element of C = scalar product of row i of A x
column j of B
25
矩陣相乘
26
27
矩陣乘法之性質
28
Example 1: Matrix Multiplication
Computer C = AB for
Solution
Because A is a 2x3 matrix and B is a 3x2 matrix, AB is defined, and C will be a 2x2 matrix.
A1
2
1
1
2
3
B
1
2
1
1
3
2
C5
7
8
11
C11
1 1 2( )
1
2
1
5 C12
1 1 2( )
1
3
2
8
C21
2 1 3( )
1
2
1
7 C22
2 1 3( )
1
3
2
11
29
Many computations that commonly occur in
operations research can be concisely expressed by using matrix multiplication.
Some important properties of matrix multiplications are: Row i of AB = (row i of A)B
Column j of AB = A(column j of B)
A1
2
1
1
2
3
B
1
2
1
1
3
2
30
Trace of a matrix
1
For any two matrices A and B. Trace(A + B) = Trace(A) + Trace(B)
For any two matrices A and B for which the product AB and BA are defined. Trace(AB) = Trace(BA)
n
iiinnij aATraceaALet
1
)( ,
p.20 #7
31
Example : Find Trace(A) & Trace(B)
029
205
950
653
542
321
B
A
32
LU Decomposition (LU 分解法 )
33
34
例題:
35
36
Use the EXCEL MMULT function to multiply the
matrices: Enter matrix A into cells B1:D2 and matrix B into cells
B4:C6.
Select the output range (B8:C9) into which the product will be computed.
In the upper left-hand corner (B8) of this selected output range type the formula: = MMULT(B1:D2,B4:C6).
Press Control-Shift-Enter
A B C D1 Matrix A 1 -1 22 2 1 334 Matrix B 1 15 2 36 1 278 A B = 1 29 7 11
(p.19)