2-1 chapter 2 matrices 2.1 operations with matrices matrix (i, j)-th entry: row: mcolumn: nsize:...
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2-1
CHAPTER 2 MATRICES
2.1 Operations with Matrices Matrix
nm
nmmnmmm
n
n
n
ij M
aaaa
aaaa
aaaa
aaaa
aA
][
321
3333231
2232221
1131211
(i, j)-th entry: ija
row: m column: n size: m×n
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i-th row vector
iniii aaar 21
j-th column vector
mj
j
j
j
c
c
c
c2
1
row matrix
column matrix
Square matrix: m = n
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Matrix form of a system of linear equations:
mnmnmm
nn
nn
bxaxaxa
bxaxaxa
bxaxaxa
2211
22222121
11212111
= = =
A x b
equationslinear m
equationmatrix Single
bx A 1 nnm 1m
mnmnmm
n
n
b
b
b
x
x
x
aaa
aaa
aaa
2
1
2
1
21
22221
11211
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Partitioned matrices:
2221
1211
34333231
24232221
14131211
AA
AA
aaaa
aaaa
aaaa
A
submatrix
3
2
1
34333231
24232221
14131211
r
r
r
aaaa
aaaa
aaaa
A
4321
34333231
24232221
14131211
cccc
aaaa
aaaa
aaaa
A
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n
mnmm
n
n
ccc
aaa
aaa
aaa
A
21
21
22221
11211
nx
x
x
x2
1
12211
2222121
1212111
mnmnmm
nn
nn
xaxaxa
xaxaxa
xaxaxa
Ax
a linear combination of the column vectors of matrix A:
linear combination of column vectors of A
mn
n
n
n
mm a
a
a
x
a
a
a
x
a
a
a
x
2
1
2
22
21
2
1
21
11
1
1c
=
2c
=
nc
=
nncxcxcx 2211
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2.2 Properties of Matrix Operations
Three basic matrix operators:
(1) matrix addition
(2) scalar multiplication
(3) matrix multiplication
Zero matrix: nm0
Identity matrix of order n: nI
100
010
001
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undefined. is defined is then , If BAABpm , (1)
mmmm MBAMABnpm , (3) then , If
nnmm MBAMABnmpm , (2) then , , If (Sizes are not the same)
(Sizes are the same, but matrices are not equal)
Matrix:
BAAB pnnm
Three situations:
ab = ba (Commutative law for multiplication)
Real number:
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(Cancellation is not valid)
0 , cbcac
b a (Cancellation law)
Matrix:
0 CBCAC
(1) If C is invertible, then A = B
Real number:
BA then ,invertiblenot is C If (2)
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nm
mnmm
n
n
M
aaa
aaa
aaa
A
If
21
22221
11211
mn
mnnn
m
m
T M
aaa
aaa
aaa
A
Then
21
22212
12111
Transpose of a matrix:
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A square matrix A is symmetric if A = AT
A square matrix A is skew-symmetric if AT = –A
Skew-symmetric matrix:
Symmetric matrix:
Note: TAA is symmetric
Proof:
symmetric is
)()(T
TTTTTT
AA
AAAAAA
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2.3 The Inverse of a Matrix
Notes: IAAAA 11
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1nEliminatioJordan -Gauss || AIIA
If A can’t be row reduced to I, then A is singular.
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IA 0(1)
0)( )2(factors
kAAAAk
k
integers:, )3( srAAA srsr rssr AA )(
Power of a square matrix:
kn
k
k
k
n d
d
d
D
d
d
d
D
00
00
00
00
00
00
)4( 2
1
2
1
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Note:
11
12
13
11321
AAAAAAAA nn
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Note:
If C is not invertible, then cancellation is not valid.
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2.4 Elementary Matrices
Note:
Only do a single elementary row operation.
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Note: If A is invertible
][][ 1123
AIIAEEEEk
IAEEEEk 123 Then
1231 EEEEA k 11
31
21
1 kEEEEA
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Note:
If a square matrix A can be row reduced to an upper triangular
matrix U using only the row operation of adding a multiple of
one row to another, then it is easy to find an LU-factorization of A.
LUA
UEEEA
UAEEE
k
k
11
21
1
12
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bLUxLUA then,If
bLyUxy then, Let
Two steps:
(1) Write y = Ux and solve Ly = b for y
(2) Solve Ux = y for x
bAx
Solving Ax=b with an LU-factorization of A