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Fin500J Mathematical Foundations in Finance
Topic 1: Matrix Algebra
Philip H. Dybvig
Reference: Mathematics for Economists, Carl Simon and Lawrence Blume, Chapter 8 Chapter 9
and Chapter 16
Slides designed by Yajun Wang
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Outline
Definition of a Matrix
Operations of Matrices
Determinants
Inverse of a Matrix
Linear System
Matrix Definiteness
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Matrix (Basic Definitions)
An k × n matrix A is a rectangular array of numbers with k rows and n
columns. (Rows are horizontal and columns are vertical.) The numbers k and n
are the dimensions of A. The numbers in the matrix are called its entries. The
entry in row i and column j is called aij .
a11 , , a1n
a21 , , a2 n
A
Aij
a , , a
kn
k1
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Operations with Matrices (Sum, Difference)
Sum, Difference
If A and B have the same dimensions, then their sum, A + B, is obtained by
adding corresponding entries. In symbols, (A + B)ij = aij + bij . If A and B
have the same dimensions, then their difference, A − B, is obtained by
subtracting corresponding entries. In symbols, (A - B)ij = aij - bij .
Example:
3 4 1 1 0 7 2 4 8
6 7 0
6 5 1
12 12 1
T hemat rix0 whose ent riesare all zero.T hen,for all A
A0 A
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Operations with Matrices (Scalar Multiple)
Scalar Multiple
If A is a matrix and r is a number (sometimes called a scalar in this
context), then the scalar multiple, rA, is obtained by multiplying every
entry in A by r. In symbols, (rA)ij = raij .
Example:
3 4 1 6 8 2
2
6 7 0
12 14 0
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Operations with Matrices (Product)
Product
If A has dimensions k × m and B has dimensions m × n, then the product
AB is defined, and has dimensions k × n. The entry (AB)ij is obtained
by multiplying row i of A by column j of B, which is done by multiplying
corresponding entries together and then adding the results i.e.,
( ai1 ai 2
b1 j
b2 j
... aim )
b
mj
ai1b1 j ai 2b2 j ... aimbmj .
Exam ple
a b
aA bC aB bD
A B
c
d
.
cA
dC
cB
dD
e f C D eA fC eB fD
1 0 0
0
1
0
Ident it ym at rixI
for any m n m at rixA, AI A and for
0 0 1
nn
any n m m at rixB, IB B.
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Laws of Matrix Algebra
The matrix addition, subtraction, scalar multiplication and matrix
multiplication, have the following properties.
Associat ive Laws :
A (B C) (A B) C, (AB)C A(BC).
Commutative Law for Addit ion:
A B B A
Dist ributive Laws :
A(B C) AB AC, (A B)C AC BC.
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Operations with Matrices (Transpose)
Transpose
The transpose, AT , of a matrix A is the matrix obtained from A by
writing its rows as columns. If A is an k×n matrix and B = AT then
B is the n×k matrix with bij = aji. If AT=A, then A is symmetric.
Example:
a11 a21
T
a
a
a
11 12 13
a12 a22
a
a
a
21 22 23
a a
13 23
It it easy t overify:
(A B)T AT B T , (A B)T AT B T ,
(AT )T A, (rA)T rAT
where A and B are k n and r is a scalar.
Let C be a k m mat rixand D be an m n mat rix.T hen,
(CD)T D T C T ,
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Determinants
Determinant is a scalar
Defined for a square matrix
Is the sum of selected products of the elements of the matrix, each
product being multiplied by +1 or -1
det( A)
a11 a12
a21 a22
a1n
a2 n
n
j 1
an1 an 2
n
aij (1) M ij aij (1)i j M ij
i j
i 1
ann
• Mij=det(Aij), Aij is the (n-1)×(n-1)
submatrix obtained by deleting row
i and column j from A.
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Determinants
a b
ad bc
The determinant of a 2 ×2 matrix A is det(A)
c d
The determinant of a 3 ×3 matrix is
a11 a12 a13
11
a21 a22 a23 a11 (1)
a31 a32 a33
a22 a23
a32 a33
12
a12 (1)
a21 a23
a31 a33
13
a13 (1)
a21 a22
a31 a32
Example
1 2 3
5 6
4 6
4 5
4 5 6 1(1)11
2(1)12
3(1)13
8 10
7 10
7 8
7 8 10
50 48 2(40 42) 3(32 35) 3
• In Matlab: det(A) = det(A)
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Inverse of a Matrix
Definition. If A is a square matrix, i.e., A has dimensions n×n. Matrix
A is nonsingular or invertible if there exists a matrix B such that
AB=BA=In. For example.
2
1 3
1
1
2
1
3
1 2 1
1 1
3 3 3
3 3 1 0
1 2 2 1 2 0 1
3 3 3 3 3
Common notation for the inverse of a matrix A is A-1
The inverse matrix A-1 is unique when it exists.
If A is invertible, A-1 is also invertible A is the inverse matrix of A-1.
(A-1)-1=A.
• Matrix division:
If A is an invertible matrix, then (AT)-1 = (A-1)T
A/B = AB-1
• In Matlab: A-1 = inv(A)
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Calculation of Inversion using Determinants
Def: For any n×n matrix A, let Cij denote the (i,j) th cofactor of A, that is, (-1)i+j
times the determinant of the submatrix obtained by deleting row i and column j
form A, i.e., Cij = (-1)i+j Mij . The n×n matrix whose (i,j)th entry is Cji, the (j,i)th
cofactor of A is called the adjoint of A and is written adj A.
Thm: Let A be a nonsingular matrix. Then,
1
A-1
adj A.
det A
thus
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Calculation of Inversion using Determinants
2 4 5
Example: find the inverse of the matrix
A 0 3 0
1 0 1
Solve:
C11
3
0
0
1
C21
4
5
0
1
4
5
3
0
C31
3, C12
0
0
1
4, C22
1
2
5
1
15, C32
0, C13
1
0
3
1
0
3, C23
2
5
0
0
0, C33
3,
2
4
1
0
2
4
0
3
4,
6,
det A 9,
C31 3
4 15
C22 C32 0
3
0 .
C23 C33
4
6
3
4 15 thus
3
Using Determinants to find the
1
0
3
0 .
inverse of a matrix can be very
9
4
6
3
complicated. Gaussian elimination is
C11
adjA C12
C
13
So,
A1
C21
more efficient for high dimension matrix.
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Calculation of Inversion using Gaussian Elimination
Elementary row operations:
o Interchange two rows of a matrix
o Change a row by adding to it a multiple of another row
o Multiply each element in a row by the same nonzero number
•
To calculate the inverse of matrix A, we apply the elementary row
operations on the augmented matrix [A I] and reduce this matrix to the
form of [I B]
•
The right half of this augmented matrix B is the inverse of A
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Calculation of inversion using Gaussian elimination
a11 , , a1n
a
,
,
a
2n
A 21
a ,, a
nn
n1
a11 , , a1n 1 0 0
a , , a2 n 0 1 0
[ A I ] 21
a , , a 0 0 1
nn
n1
I is the identity matrix, and use Gaussian elimination to obtain a matrix of the form
1 0 0 b11 b12 b1n
0 1 0 b21 b22 b2 n
0 0 1 b b b
n1 n 2
nn
The matrix
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b11 b12 b1n
b b b2 n
B 21 22
b b b
nn
n1 n 2
is then the matrix inverse of A
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Example
1
A 12
3
1
2
4
1 1 1 |1 0 0
[ A | I ] 12 2 3 | 0 1 0
3 4 1 | 0 0 1
1
3
1
(ii)+(-12)×(i), (iii)+(-3) ×(i), (iii)+(ii) ×(1/10)
1 | 1
0 0
1 1
0
10
15
|
12
1
0
0 0 3.5 | 4.2 0.1 1
The matrix
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3
1
0.4
35
7
3
0.6 2
35 7
1
2
1.2
35
7
1
0
0
0
1
0
3
1
35
7
2
3
0 | 0.6
35 7
1
2
1 | 1.2
35
7
0 | 0.4
is then the matrix inverse of A
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Systems of Equations in Matrix Form
The system of linear equations
a11 x1 a12 x2 a13 x3
a1n xn b1
a21 x1 a22 x2 a23 x3
a2 n xn b2
ak1 x1 ak 2 x2 ak 3 x3
akn xn bk
can be rewritten as the matrix equation Ax=b, where
a11
A
a
k1
x1
b1
a1n
x
b2
2
, x , b .
akn
xn
bk
If an n×n matrix A is invertible, then it is nonsingular, and the
unique solution to the system of linear equations Ax=b is x=A-1b.
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Example: solve the linear system
4 x y 2 z 4
5 x 2 y z 4
x 3z 3
Matrix Inversion
AX db
4 1 2
x
4
A 5 2 1 ; X y ; b 4
1 0 3
z
3
X A1b
• In Matlab
>>x=inv(A)*b
or
>> x=A\b
6 -3 -3
1
A -1 -14 10 6
6
-2 1 3
x
6 -3 -3 4
y 1 -14 10 6 4
6
z
-2 1 3 3
x 1 2; y 1 3; z 5 6
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Matrix Operations in Matlab
>> A=[2 3; 1 1; 1 0]
A =
Sum
>> A+B1
ans =
2
3
3
4
1
1
1
2
1
0
3
4
>> B1=[1 1; 0 1; 2 4]
Difference
B1 =
>> A-B1
ans =
1
1
1
2
0
1
1
0
2
4
-1
-4
>> B2=[1 1 1; 1 0 2]
Product
B2 =
>> A*B2
ans =
1
1
1
5
2
8
1
0
2
2
1
3
1
1
1
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Matrix Operations in Matlab
transpose
>> C'
ans =
>> C=[1 1 1; 12 2 -3;
3 4 1]
C =
1
1
1
12
2
-3
3
4
1
determinant
1
12
3
1
2
4
1
-3
1
>> det(C)
ans =
35
>> inv(C)
inverse
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ans =
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0.4000
0.0857
-0.1429
-0.6000
-0.0571
0.4286
1.2000
-0.0286
-0.2857
20
Positive Definite Matrix
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Negative Definite, Positive Semidefinite, Negative
Semidefinite, Indefinite Matrix
Let A be an N×N symmetric matrix, then A is
•
•
•
•
negative definite if and only if vTAv <0 for all v≠0 in RN
positive semidefinite if and only if vTAv ≥0 for all v≠0, in RN
negative semidefinite if and only if vTAv ≤0 for all v≠0, in
RN
indefinite if and only if vTAv >0 for some v in RN and <0 for
other v in RN
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