NUMERICAL METHODS
NUMERICAL METHODS
AND PROGRAMMING
AND PROGRAMMING
REVIEW OF MATRIX ALGEBRA.
METHODS OF SOLVING SETS
OF LINEAR EQUATIONS.
Joanna Iwaniec
[ ]
=
×
mn
m
m
n
n
n
m
a
a
a
a
a
a
a
a
a
A
L
M
O
M
M
L
L
2
1
2
22
21
1
12
11
Real / complex / square matrix, equal matrices
Matrix summation, matrix product
Elements of matrix product C = AB, where are defined as
follows:
(
) (
)
p
n
B
n
m
A
×
×
,
Column vector, set of linear equations
Set of linear equations, linear dependence
Identity matrix
Basis for R
n
, solutions of linear system of equations
Inverse / nonsingular / singular matrices
A square matrix
A square matrix
A
A
(
(
R
R
nxn
nxn
) is nonsingular if
) is nonsingular if
A
A
x = b has a unique solution for
x = b has a unique solution for
all values of b. (Note that the definition only applies to squar
all values of b. (Note that the definition only applies to squar
e matrices.)
e matrices.)
or
Inverse / nonsingular / singular matrices
↔
Other equivalent definitions of nonsingular
matrices
A is nonsingular if and only if A
T
is nonsingular.
A is nonsingular if and only if det(A) ≠ 0, where det(A) denotes
the determinant of A.
A is nonsingular if and only if it has an inverse (see below).
Nonsingular matrices are also called invertible matrices.
A is nonsingular if and only Ax = 0 implies x = 0. (In other words,
there exists no nonzero x that satisfies Ax = 0.)
Useful properties of properties of matrix inverses
If a matrix has an inverse, it is unique: if BA = I and CA = I, then
B = C = A
-1
.
The inverse A
-1
satisfies AA
-1
= I. This is an example where the
matrix product of two matrices commutes: AA
-1
= A
-1
A = I: (Recall
that for general matrices, AB ≠ BA).
Inverse of the transpose
: if A is square and invertible, then
(A
T
)
-1
= (A
-1
)
T
Because the order of transpose and inverse does not matter we usually
write the inverse of the transpose as A
-T
.
Inverse of product
: if A and B are square and invertible, then
(AB)
-1
= B
-1
A
-1
Determinant of matrix (det(A), |A|)
How to calculate |A|?
Next slide
Inverse of matrix A
Cramer’s Rule
‘Special’ matrices
Lower triangular matrices
A (R
n x n
) is lower triangular if aij = 0, for j > i.
A lower triangular matrix is called unit lower triangular if the diagonal
elements are equal to one.
A lower triangular matrix is nonsingular if and only if aii ≠ 0 for all i.
Upper triangular matrices
A matrix A (R
n x n
) is upper triangular if A
T
is lower triangular, i.e., if
aij = 0 for j < i.
we might define the norm of an m X n matrix A as the square root of
the sum of the squares of the elements of A:
The norm of a matrix serves the same purpose as the norm of a vector - it is
a measure of the size or magnitude of the matrix.
As for vectors, many possible definitions exist. E. g., in analogy with the
Euclidean norm of a vector x,
Matrix norms
This is called the Frobenius norm of A.
Other Useful Norms
For Vectors
- the p - norm
∑
=
=
n
i
i
x
A
1
1
- sum of the absolute values
of the element
p
p
n
i
i
p
x
A
1
1
=
∑
=
i
n
i
i
x
A
≤
≤
=
max
- maximum magnitude norm
For Matrices
∑
=
≤
≤
=
n
i
ij
n
j
A
A
1
1
max
- column sum norm
- row sum norm
∑
=
≤
≤
=
n
j
ij
n
j
A
A
1
1
max
Matrix norms - continued
The norm of a square or rectangular matrix A (R
m x n)
, denoted ||A||,
is defined as:
Properties of matrix norms
Condition number of a matrix
Properties of matrix condition number
Matrix A is:
well-conditioned if its condition number is small,
ill-conditioned if its condition number is large.
EXAMPLE:
Equations that are not properly scaled may
give rise to large condition numbers.
k (A) = || A || || A
-1
|| = 2000 × 0.0005 = 1001(large)
Consider
−
=
1000
1000
1
1
A
−
=
−
0005
.
0
5
.
0
0005
.
0
5
.
0
A
1
EQUILIBRATION:
Scale each row of A by its largest element.
−
=
1
1
1
1
A
−
=
−
5
.
0
5
.
0
5
.
0
5
.
0
A
1
K(A) = ||A|| ||A
-1
|| = 2(1) = 2
Great Improvement in the condition number!
Sets of linear equations – methods
for solution finding
back substitution method (for triangular systems of linear equations)
Cramer’s method, Gaussian elimination, LU, LL
T
, LDL
T
, QR
matrix decomposition methods,
← fast but sometimes obtained
solution is not accurate
iterative methods
methods operating on sparse matrices
←small computational effort
iterative methods
block methods
Solving sets of linear equations - introduction
Selection of a solution estimation method should depend on:
Selection of a solution estimation method should depend on:
Form of the matrix A,
Type of problem that is
represented by the considered
set of linear equations.
accuracy of obtained solution,
computational effort.
Back substitution
Triangular systems of equations – back
substitution
=
=
+
=
+
+
+
=
+
+
+
+
−
−
−
−
−
−
−
−
−
n
n
nn
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
f
x
u
f
x
u
x
u
f
x
u
x
u
x
u
f
x
u
x
u
x
u
x
u
1
,
1
1
1
,
1
2
2
1
1
,
2
2
22
1
1
1
1
,
1
2
12
1
11
M
O
K
K
Example
=
=
−
=
+
−
8
4
7
3
3
5
2
3
3
2
3
2
1
x
x
x
x
x
x
I
II
Triangular systems of equations – back
substitution
nn
n
n
a
b
x
=
1
,
,
2
,
1
,
1
1
K
K
−
−
=
−
−
−
=
+
+
n
n
i
a
x
a
x
a
b
x
ii
i
ii
n
in
i
i
n
n
M
2
1
2
1
2
+
=
n
n
D
2
1
2
1
2
−
=
Number of multiplications and divisions
Number of summations
Cramer’s method
Cramer’s method
=
+
=
+
2
2
22
1
21
1
2
12
1
11
b
x
a
x
a
b
x
a
x
a
−
−
=
−
−
=
12
21
22
11
1
21
2
11
2
12
21
22
11
2
12
1
22
1
a
a
a
a
b
a
b
a
x
a
a
a
a
b
a
b
a
x
Gaussian elimination
Gaussian elimination
Using row operations, the matrix is reduced to an upper triangular
matrix and the solutions obtained by back substitution.
Partial Pivot Method
Step 1:
We look for the largest element in absolute value (called pivot) in
the first column and interchange this row with the first row.
Step 2:
Multiply the I row by an appropriate number and subtract
this from the II row so that the first element of the second row
becomes zero.
Do this for the remaining rows so that all the elements in the I
column except the top most element are zero.
Now the matrix looks like:
Now look for the pivot in the second column below the first row
and iinterchange this with the second row.
Multiply the II row by an appropriate numbers and subtract this
from remaining rows (below the second row) so that the matrix looks
like :
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
0
0
0
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
0
0
0
0
0
Now look for the pivot in the III column below the second row and
interchange this row with the third row. Proceed till the matrix looks
like :
The solution is obtained by back substitution.
x
x
x
x
x
x
x
x
x
x
x
x
x
x
0
0
0
0
0
0
Gauss Elimination – example 1
−
=
−
2
1
1
x
x
x
8
1
4
3
1
2
2
0
1
3
2
1
−
−
2
8
1
4
1
3
1
2
1
2
0
1
Augmented
Matrix
−
−
2
8
1
4
1
3
1
2
1
2
0
1
−
−
1
2
0
1
1
3
1
2
2
8
1
4
−
5
.
0
0
25
.
0
0
2
1
5
.
1
0
2
8
1
4
8333
.
0
1666
.
0
0
0
2
1
5
.
1
0
2
8
1
4
Solution obtained by back substitution
8333
.
0
1667
.
1
0
0
2
1
5
.
1
0
2
8
1
4
−
=
+
⋅
−
=
−
=
−
=
=
=
9
4
2
5
8
2
2
5
,
1
5
2
5
1667
,
1
8333
,
0
1
2
3
x
x
x
=
−
=
−
=
5
2
9
3
2
1
x
x
x
Gaussian elimination – example 2
−
=
−
+
−
=
+
+
−
=
+
−
3
2
4
3
2
3
3
2
1
3
2
1
3
2
1
x
x
x
x
x
x
x
x
x
−
=
−
−
=
+
−
=
+
−
1
3
7
3
5
3
3
1
3
4
3
2
3
3
2
3
2
3
2
1
x
x
x
x
x
x
x
=
−
−
=
+
−
=
+
−
4
11
4
11
3
3
1
3
4
3
2
3
3
3
2
3
2
1
x
x
x
x
x
x
Use 1
st
equation to eliminate x
1
from 2
nd
and 3
rd
equations
Use 2
nd
equation to eliminate
x
2
from the 3
rd
equation
1. Input matrix A of order n
×
n and n
×
1 vector B and form the
augmented matrix [A|B].
2. For j = 1 to n do
{ (a) Compute the pivot index j
≤
p
≤
n such that
Gauss Elimination (Pseudo Code)
{ }
ij
n
j
i
pj
A
max
A
=
=
(b) If A
pj
= 0 , print “singular matrix” and exit.
(c) If p > j , interchange rows p and j
(d) For each i > j , subtract A
ij
/A
jj
times row j from row i. }
3. Now the augmented matrix is [C|D] where C is upper triangular.
4. Solve by back substitution
For j = 1= n down to 1 compute:
−
=
∑
+
=
n
1
j
i
i
ji
j
jj
j
x
D
D
C
1
x
Matrix Determinant with the use of Gauss
Elimination
Gauss Elimination procedure uses two types of row operations.
1. Interchange of rows for pivoting
2. Add
∝
times row j to row k.
The first operation changes the sign of the determinant while the
second has no effect on the determinant.
To obtain the matrix determinant, follow Gauss elimination and
obtain the upper triangular form U, keeping count of (r) the
interchanges of rows. Then:
det A = (-1)
r
U
11
U
22
... U
nn
Matrix Determinant (Pseudo Code)
1. Set r = 0
2. For j = 1 to n do
{ (a) Compute the pivot index j
≤
p
≤
n such that
}
A
{
max
A
ij
n
j
i
pj
=
=
3. det = (-1)
r
A
11
A
22
... A
nn
(b) If A
pj
= 0 , print “Singular Matrix” and exit.
(c) If p > j interchange rows p and j , set r = r +1.
(d) For each i > j , subtract A
ij
/A
jj
times row j and row i
}
Determinant of a Matrix
Some properties of the determinant.
1. det (A
T
) = det (A)
2. det (AB) = det (A) det (B)
3. det (U) = U
11
U
22
U
33
...U
nn
Matrix Determinant - Example
0
r
1
0
1
6
2
0
0
4
1
=
−
=
A
0
r
1
4
0
6
2
0
0
4
1
=
1
r
6
2
0
1
4
0
0
4
1
=
det = (-1)
1
(1)(4)(5,5) = -22
1
r
5
,
5
0
0
1
4
0
0
4
1
=
LU matrix decomposition methods
LU Decomposition
When Gauss elimination is used to solve a linear system
of equations, the row operations are applied
simultaneously to the right side (
RS
) of the considered
system of equations.
If the system is solved again for a different
RS
vector,
the row operations must be repeated.
The LU decomposition eliminates the need to repeat the
row operators each time a new
RS
vector is used.
The matrix A (Ax = b) is written as a product of an
upper
upper
triangular matrix U
triangular matrix U
and a
lower triangular matrix L
.
Diagonal elements of the upper triangular matrix are chosen to be 1
to get a consistent system of equations.
n = 3
⋅
=
1
0
0
U
1
0
U
U
1
L
L
L
0
L
L
0
0
L
23
13
12
33
32
31
22
21
11
33
32
31
23
22
21
13
12
11
A
A
A
A
A
A
A
A
A
9 Equations for 9 unknowns L
11
, L
21
, L
22
, L
31
, L
32
, L
33
,
U
12
, U
13
, U
23.
LU Decomposition
=
+
+
+
+
+
33
32
31
23
22
21
13
12
11
33
23
32
13
31
32
12
31
31
23
22
13
21
22
12
21
21
13
11
12
11
11
L
U
L
U
L
L
U
L
L
U
L
U
L
L
U
L
L
U
L
U
L
L
A
A
A
A
A
A
A
A
A
First column gives (L
11
, L
21
, L
31
) = (A
11
, A
21
, A
31
)
First row gives (U
11
, U
12
, U
13
) = (1, A
12
/L
11
, A
13
/L
11
)
Second column gives (L
12
, L
22
, L
32
) = (0, A
22
- L
21
U
12
, A
32
- L
31
U
12
)
Second row gives (U
21
, U
22
, U
23
) = (0, 1, (A
23
- L
21
U
13
)/L
22
)
Third column gives (L
13
, L
23
, L
33
) = (0, 0, A
33
- L
31
U
13
- L
32
U
23
)
LU Decomposition
−
−
=
−
=
=
=
−
=
=
=
=
=
23
32
13
31
33
33
12
31
32
32
31
31
23
12
21
22
22
21
21
13
12
11
11
0
0
0
U
L
U
L
A
L
U
L
A
L
A
L
L
U
L
A
L
A
L
L
L
A
L
First column gives (L
11
, L
21
, L
31
) = (A
11
, A
21
, A
31
)
First row gives (U
11
, U
12
, U
13
) = (1, A
12
/L
11
, A
13
/L
11
)
Second column gives (L
12
, L
22
, L
32
) = (0, A
22
- L
21
U
12
, A
32
- L
31
U
12
)
Second row gives (U
21
, U
22
, U
23
) = (0, 1, (A
23
- L
21
U
13
)/L
22
)
Third column gives (L
13
, L
23
, L
33
) = (0, 0, A
33
- L
31
U
13
- L
32
U
23
)
LU Decomposition
(
)
=
=
=
−
=
=
=
=
=
=
1
0
0
/
1
0
/
/
1
33
32
31
22
13
21
23
23
22
21
11
13
13
11
12
12
11
U
U
U
L
U
L
A
U
U
U
L
A
U
L
A
U
U
LU Decomposition (Pseudo Code)
L
i1
= A
i1
,
i = 1, 2, ... , n
U
1j
= A
1j
/L
11
,
j = 2, 3, ... , n
for j = 2, 3, ... , n-1
n
,
...
1,
j
j,
i
,
U
L
A
L
1
-
j
1
k
kj
ik
ij
ij
+
=
−
=
∑
=
n
,
...
1,
j
j,
k
,
L
U
L
A
U
jj
1
1
i
ik
ji
jk
jk
+
=
−
=
∑
−
=
j
∑
−
=
−
=
1
1
kn
nk
nn
nn
U
L
A
L
n
k
LU Decomposition (Example)
[ ] [ ] [ ]
?
]
[
?
2
4
3
1
3
1
1
5
2
=
=
⋅
=
−
−
−
−
U
L
U
L
−
−
=
−
=
=
=
−
=
=
=
=
=
23
32
13
31
33
33
12
31
32
32
31
31
23
12
21
22
22
21
21
13
12
11
11
0
0
0
U
L
U
L
A
L
U
L
A
L
A
L
L
U
L
A
L
A
L
L
L
A
L
(
)
=
=
=
−
=
=
=
=
=
=
1
0
0
/
1
0
/
/
1
33
32
31
22
13
21
23
23
22
21
11
13
13
11
12
12
11
U
U
U
L
U
L
A
U
U
U
L
A
U
L
A
U
U
−
−
−
−
=
−
−
=
=
=
+
=
−
=
=
=
=
23
12
13
33
12
32
31
23
12
22
21
13
12
11
)
3
4
(
3
2
3
4
3
0
3
1
0
0
2
U
U
U
L
U
L
L
L
U
L
L
L
L
L
(
)
=
=
=
−
−
−
−
=
=
=
=
−
=
=
1
0
0
)
2
/
5
3
/(
)
2
/
1
)(
1
(
1
1
0
2
/
1
2
/
5
1
33
32
31
23
22
21
13
12
11
U
U
U
U
U
U
U
U
U
−
=
4
5
,
3
3
0
2
/
1
1
0
0
2
L
−
−
=
1
0
0
1
1
0
2
/
1
2
/
5
1
U
LU Decomposition (Example)
−
−
⋅
−
=
−
−
−
−
1
0
0
1
1
0
5
.
0
5
.
2
1
4
5
.
3
3
0
5
.
0
1
0
0
2
2
4
3
1
3
1
1
5
2
[
]
=
=
nn
n3
n2
n1
2n
23
22
21
1n
13
12
11
L
...
L
L
L
U
...
U
L
L
U
...
U
U
L
\
Q
M
M
M
M
M
M
M
U
L
The value of U are stored in the zero space of L .
After each element of A is employed , it is not needed again. So Q
can be stored in place of A.
After LU decomposition of the matrix A, the system AX = B is
solved in 2 steps : (1) Forward substitution, (2) Backward
Substitution
AX = LUX = B
Put UX = Y
Forward Substitution : Y
1
= B
1
/ L
11
Y
2
= (B
2
- L
21
Y
1
) / L
22
Y
3
= (B
3
- L
31
B
1
- L
32
B
2
) / L
33
=
=
3
2
1
3
2
1
33
32
31
22
21
11
Y
Y
Y
L
L
L
0
L
L
0
0
L
B
B
B
LY
=
=
3
2
1
3
2
1
23
13
12
Y
Y
Y
X
X
X
1
0
0
U
1
0
U
U
1
UX
Back Substitution :
X
3
= Y
3
X
2
= Y
2
- X
3
U
23
X
1
= Y
1
- X
2
U
12
- X
3
U
23
As in Gauss elimination, LU decomposition must employ pivoting to
avoid division by zero and to minimize round off errors. The pivoting
is done immediately after computing each column.
Matrix Inverse Using the LU Decomposition
LU decomposition can be used to obtain the inverse of
the original coefficient matrix.
Each column j of the inverse is determined by using a
unit vector (with 1 in the j
th
row ) as the RHS vector
−
−
−
=
−
−
−
−
1
0
0
1
1
0
5
.
0
5
.
2
1
4
5
.
3
3
0
5
.
0
1
0
0
2
2
4
3
1
3
1
1
5
2
L
U
A
Matrix inverse using LU decomposition-an example
First column of the inverse of A is given by
=
−
−
−
0
0
1
1
0
0
1
1
0
5
.
0
5
.
2
1
4
5
.
3
3
0
5
.
0
1
0
0
2
3
2
1
x
x
x
−
=
→
=
−
25
.
1
1
5
.
0
0
0
1
4
5
.
3
3
0
5
.
0
1
0
0
2
3
2
1
3
2
1
d
d
d
d
d
d
−
−
=
→
=
−
−
25
.
1
25
.
0
5
.
0
x
x
x
25
.
1
1
5
.
0
x
x
x
1
0
0
1
1
0
5
.
0
5
.
2
1
3
2
1
3
2
1
The second and third columns are obtained by taking the RS vector as
(0,1,0) and (0,0,1).
Solution of set of linear equations by LU
Decomposition
where:
where:
A
A
–
–
presumed to be square,
presumed to be square,
non
non
-
-
singular
singular.
Suppose we can decompose
Suppose we can decompose
A
A
into
into
A
A
=
=
LU
LU
where:
where:
L
L
–
–
lower triangular with diagonal elements equal to 1,
lower triangular with diagonal elements equal to 1,
U
U
–
–
upper triangular.
upper triangular.
Procedure of Factorization of A
LL
T
Banachiewicz / Cholesky decomposition
For symmetrical, positively definite matrix, Banachiewicz /
Cholesky decomposition into triangular matrices can be defined:
T
LL
A
=
where:
where:
L
L
–
–
lower triangular matrix with
lower triangular matrix with
positive
positive
diagonal elements
diagonal elements
(
(
non necessarily equal to 1
non necessarily equal to 1
),
),
We have to compute in succession:
+
+
=
−
=
=
−
=
∑
∑
−
=
−
=
n
i
i
j
l
l
l
a
l
n
i
l
a
l
ii
i
k
ik
jk
ji
ji
i
k
ik
ii
ii
,
,
2
,
1
,
,
,
2
,
1
,
1
1
1
1
2
K
K
LL
T
Banachiewicz / Cholesky decomposition -
example
=
6
3
2
3
5
2
2
2
4
A
1
2
/
2
,
1
2
/
2
,
2
4
:
1
31
21
11
=
=
=
=
=
=
=
l
l
l
i
(
)
1
2
/
1
1
3
,
2
1
1
5
:
2
32
22
=
⋅
−
=
=
⋅
−
=
=
l
l
i
2
1
1
1
1
6
:
3
33
=
⋅
−
⋅
−
=
=
l
i
⋅
=
2
0
0
1
2
0
1
1
2
2
1
1
0
2
1
0
0
2
6
3
2
3
5
2
2
2
4
Iterative Method for solution of Linear
systems
Jacobi iteration
Gauss - Seidel iteration
Jacobi Iteration
AX = B
n = 3
a
11
x
1
+ a
12
x
2
+ a
13
x
3
= b
1
a
21
x
1
+ a
22
x
2
+ a
23
x
3
= b
2
a
31
x
1
+ a
32
x
2
+ a
33
x
3
= b
3
Preprocessing :
1. Arrange the equations so that the diagonal terms of the coefficient
matrix are not zero.
2. Make row transformation if necessary to make the diagonal
elements as large as possible.
Rewrite the equation as
x
1
= 1/a
11
(b
1
- a
12
x
2
- a
13
x
3
)
x
2
= 1/a
22
(b
2
- a
21
x
1
- a
23
x
3
)
x
3
= 1/a
33
(b
3
- a
31
x
1
- a
32
x
2
)
Choose an initial approximation vector (x
0
, x
1
, x
2
).
If no prior information is available then (x
0
, x
1
, x
2
) = (0, 0, 0)
will do.
Iterate :
n
i
x
A
b
a
x
i
j
k
j
ij
i
ii
k
i
≤
≤
−
=
∑
≠
+
1
),
(
1
1
Stop when
<∈
−
−
100
j
i
i
j
i
j
i
x
x
x
Jacobi Pseudo Code
Diagonally Dominant Matrices
∑
≠
=
>
n
i
j
j
ij
ii
a
a
1
ie., the diagonal element of each row is larger than the sum of the
absolute values of the other elements in the row.
Diagonal dominance is a sufficient but not necessary condition for
the convergence of the Jacobi or Gauss-Seidel iteration.
The Gauss-Seidel iteration
In the Jacobi iteration all the components of the new estimate of
the vector
(
)
k
n
k
k
k
x
x
x
x
,...,
,
,
3
2
1
are computed from the current estimate
(
)
k
n
k
k
k
x
x
x
x
,...,
,
,
3
2
1
However when is computed the updated estimates
are already available.
( )
1
+
k
i
x
(
)
1
1
1
2
1
,...,
,
+
−
+
+
k
i
k
k
i
x
x
x
The Gauss - Seidel iteration takes advantage of the latest
information available where updating an estimate.
Gauss - Seidel
Jacobi
First Iteration
x
1
= (b
1
- a
12
x
2
- a
13
x
3
) / a
11
x
2
= (b
2
- a
21
x
1
- a
23
x
3
) / a
22
x
3
= (b
3
- a
31
x
1
- a
32
x
2
) / a
33
x
1
= (b
1
- a
12
x
2
- a
13
x
3
) / a
11
x
2
= (b
2
- a
21
x
1
- a
23
x
3
) / a
22
x
3
= (b
3
- a
31
x
1
- a
23
x
3
) / a
33
Second Iteration
x
1
= (b
1
- a
12
x
2
- a
13
x
3
) / a
11
x
2
= (b
2
- a
21
x
1
- a
23
x
3
) / a
22
x
3
= (b
3
- a
31
x
1
- a
32
x
2
) / a
33
x
1
= (b
1
- a
12
x
2
- a
13
x
3
) / a
11
x
2
= (b
2
- a
21
x
1
- a
23
x
3
) / a
22
x
3
= (b
3
- a
31
x
1
- a
32
x
2
) / a
33
Matrix algebra in MATLAB
Essentials
Syntax & Logic Help
>>
help <function_name>
help fzero
>>
lookfor <keyword>
lookfor bessel
www.mathworks.com
Support (Documentation) & Forum Sections
Useful Tips
>>
clear (variable)
Clears All Memory or Specified Variable
>>
clc
Clears Command Window Screen
Philosophy of Data Storage
Numerical Values
(Ex. 5, 3.14159, 2+i)
Integers
Floating (Single & Double)
Complex
Character Strings
(Ex. ‘asdf jkl;’)
Structures
Cells
Boolean
(True / False)
Elementary Operators
Assignment (=)
>> X = 1;
>> Y = 3.14159
Addition (+)
>> Z = X + Y
Z = 4.14159
Subtraction (-)
>> Z = X – Y
Z = -2.14159
Multiplication (*)
>> Z = 2 * Y
Z = 6.28318
Division (/)
>> Z = 1 / 4
Z = 0.25
Power (^)
>> Z = 5 ^ 3
Z = 125
Vector Operators – Scalar Operations
Assignment
– Column
>> X = [ 1 ; 2 ; 3 ];
– Row
>> Y = [ 1 , 2 , 3 ];
>> Y = [ 1 2 3 ];
– Unique Commands
– linspace( Initial , Final , # Points )
>> Z = linspace( 5 , 20 , 4 )
-Z = [ 5 , 10 , 15 , 20 ];
– logspace( Initial , Final , # Points )
– Initial:Step:Final
>> Z = 1:-0.25:0
>> Z = [ 1 , 0.75 , 0.5 , 0.25 , 0 ]
Index
>> Z = X(3)
Z = 3
>> Z = X(4)
–OR–
>> Z = X(0)
ERROR !!
>> Z = X(1:2)
Z = [ 1 ; 2 ]
>> Z = X([ 1 3 ])
Z = [ 1 ; 3 ]
Vector Operators – Scalar Operations
Addition (+)
>> Z = X + 2
Z = [ 3 ; 4 ; 5 ]
Subtraction (-)
Multiplication (*)
>> Z = 2 * X
Z = [ 2 ; 4 ; 6 ]
Division (/)
>> Z = X / 2
Z = [ 0.5 ; 1 ; 1.5 ]
Summation
>> X = [ 1 , 2 , 3 , 4 ];
>> Z = sum( X );
Z = 10
Product
>> Y = [ 1 ; 2 ; 3 ; 4 ];
>> Z = prod( Y )
Z = 24
∏
=
n
i
i
x
1
∑
=
n
i
i
x
1
Vector Operators
Inner Product
Euclidean Norm
>> X = [ 1 , 2 , 3 ];
>> Y = [ 1 ; 2 ; 3 ];
>> Z = X * Y
Z = 14
[
]
∑
=
=
=
⋅
n
i
i
i
n
i
n
i
y
x
y
y
y
x
x
x
y
x
1
1
1
M
M
L
L
v
v
∑
=
=
⋅
=
n
i
i
x
x
x
x
1
2
v
v
v
>> X = [ 1 , 2 , 3 ];
>> Z = X * X’;
Z = 14
Cross Product
Cross Product
>> X = [ 1 , 2 , 3 ];
>> X = [ 1 , 2 , 3 ];
>> Y = [ 3 , 2 , 1 ];
>> Y = [ 3 , 2 , 1 ];
>> Z = cross( X , Y )
>> Z = cross( X , Y )
[
]
2
1
2
1
1
3
1
3
3
2
3
2
x
y
y
x
x
y
y
x
x
y
y
x
y
x
−
−
−
=
×
r
r
Vector Operators – Element Wise
Addition (+)
>> X = [ 1 , 2 , 3 ];
>> Y = [ 2 , 4 , 6 ];
>> Z = X + Y
Z = [ 3 , 6 , 9 ];
Subtraction (-)
Multiplication (.*)
>> X = [ 1 , 2 , 3 ];
>> Y = [ 2 , 4 , 6 ];
>> Z = X.*Y
Z = [ 2 , 8 , 18 ];
Division (./)
Power (.^)
>> Z = X.^2
Z = [ 1 , 4 , 9 ];
Numerical Integration
Algorithms
Trapezoidal Rule
Simpson’s Rule
Etc.
Data ( y = x^2 )
Data ( y = x^2 )
(
(
-
-
6, 36 )
6, 36 )
(
(
-
-
3 , 9 )
3 , 9 )
( 0 , 0 )
( 0 , 0 )
( 3 , 9 )
( 3 , 9 )
( 6 , 36 )
( 6 , 36 )
Code
Code
>> X = [
>> X = [
-
-
6 ,
6 ,
-
-
3 , 0 , 3 , 6 ];
3 , 0 , 3 , 6 ];
>> Y = [ 36 , 9 , 0 , 9 , 36 ];
>> Y = [ 36 , 9 , 0 , 9 , 36 ];
>> Z = sum(
>> Z = sum(
trapz
trapz
( X , Y ) );
( X , Y ) );
Z = 162
Z = 162
>> Z = quad( inline(
>> Z = quad( inline(
‘
‘
x.^2
x.^2
‘
‘
) ,
) ,
-
-
6
6
…
…
6 )
6 )
Z = 144
Z = 144
Numerical Integration
0
10
20
30
40
-6
-4
-2
0
2
4
6
X
Y
Standard Deviation
>> Y = std( X )
Sort
>> Y = sort( X )
Find
>> Y = X( find( X > 0.5 ) )
Statistics Functions
Mean
>> X = rand(10);
>> Y = mean( X )
Median
>> Y = mean( X )
Maximum
>> Y = max( X )
Minimum
n
x
x
n
i
i
∑
=
=
1
(
)
n
x
x
n
i
i
∑
=
−
=
1
2
σ
Matrix Operators – Scalar Operations
Assignment
Assignment
>> X = [ 1 , 2 ; 3 , 4 ];
Concatenation
Concatenation
>> X = 1:3;
>> Y = [ X ; 2*X ]
Y = [ 1 , 2 , 3 ; 2 , 4 , 6 ]
Index
Index
>> Z = X(1,2)
–OR–
>> Z = X(3)
Z = 3
>> Z = X(2,3)
–OR–
>> Z = X(5)
ERROR !!
Unique Functions
Identity Matrix
eye( Column , Row )
>> X = eye(2,2)
X = [ 1 , 0 ; 0 , 1 ]
Zeros & Ones Matrix
zeros( Column , Row )
ones( Column , Row )
Random # ( 0 – 1 )
rand( Column , Row )
>> X = rand( 2 , 2 )
X = [ 0.5234 , 0.9246 ;
0.2862 , 0.7378 ]
Matrix Operators – Scalar / Element Wise
Scalar Operations
– Addition (+)
>> Z = X + 2
Z = [3 , 4 ; 5 , 6]
– Subtraction (-)
– Multiplication (*)
>> Z = 2 * X
Z = [ 2 , 4 ; 6 , 8 ]
– Division (/)
Element Wise Operations
– Addition (+)
>> X = [ 1 , 2 ; 3 , 4 ];
>> Y = [ 4 , 3 ; 2 , 1 ];
>> Z = X+Y
Z = [ 5 , 5 ; 5 , 5 ]
– Subtraction (-)
– Multiplication
>> Z = X.*Y
Z = [ 4 , 6 ; 6 , 4 ]
– Division
Matrix Operations - A x = b
b = ?
>> A = [ 1 , 2 ; 3 , 4 ];
>> x = [ 1 ; 2 ];
>> b = A*x
b = [ 5 ; 11 ];
x = ?
>> A = [ 1 , 2 ; 3 , 4 ];
>> b = [ 5 ; 11 ];
>> x = A\b
x = [ 1 ; 2 ];
=
=
=
⋅
∑
∑
∑
=
=
=
n
i
n
i
i
n
i
i
n
i
i
n
i
b
b
b
x
x
x
x
x
x
x
M
M
M
M
M
M
L
L
M
O
M
N
M
L
L
M
N
M
O
M
L
L
v
1
1
i
n,
1
i
j,
1
i
1,
1
n
n,
i
n,
n,1
n
i,
i
i,
i,1
n
1,
i
1,
1,1
A
A
A
A
A
A
A
A
A
A
A
A
A
Algorithms
…
Gaussian
Elimination
LU Factorization
Etc.
Matrix Operations
Inverse
>> X = [ 1 2 3 ; 2 3 1 ; 3 1 2];
>> Y = inv(X);
>> Z = X*Y
Z = [ 1 0 0 ; 0 1 0 ; 0 0 1 ]
Determinant
>> Z = det( X )
Z = -18
– EXAMPLE (2 x 2)
– EXAMPLE (3 x 3)
2
,
1
1
,
2
2
,
2
1
,
1
x
x
x
x
−
=
X
1
,
2
2
,
2
3
,
1
1
,
2
2
,
3
3
,
1
3
,
3
1
,
2
2
,
1
1
,
3
3
,
2
2
,
1
3
,
2
2
,
3
1
,
1
3
,
3
2
,
2
1
,
1
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
−
+
−
+
−
=
L
X
Eigenstates
>> [ V , D ] = eig(Z)
Eigenvalues
D = [-1.73, 1.73, 6.00]
Eigenvectors
V = [ 0.79
0.21 0.58;
-0.21 -0.79 0.58;
-0.58 0.58 0.58]
x
Ax
α
=
Linear Regression
Data …
– ( 2, 3 )
– ( 4, 7 )
– ( 5, 10 )
– ( 6, 10 )
– ( 7, 12 )
QR Factorization
>> X = [ 2 ; 4 ; 5 ; 6 ; 7 ] ;
>> Y = [ 3 ; 7 ; 10 ; 10 ; 12 ];
>> A = [ X , ones(size(X)) ];
>> COEF = A \ Y
COEF = [ 1.7838 , -0.1622 ]
Polyfit
>> COEF = polyfit( X , Y , 1 )
COEF = [ 1.7838 , -0.1622 ]
Concept of Transform
Instead of Nonlinear Regression …
Linearize the Equation
Exponentials
• Y = a*e^(b*X)
• ln(Y) = ln(a) + b*X
Power Law
• Y = a*X^b …
• ln(Y) = ln(a) + b*ln(X)
Character Strings
Assignment
>> X = ‘HELLO’;
>> Y = 2.7183;
>> Z = num2str( Y )
Z = ‘2.7183’
Concatenation
>> X = strcat( ‘HELLO’ , ‘ WORLD’ )
-OR-
>> X = [‘HELLO’ ‘WORLD’]
X = ‘HELLOWORLD’
Comparison
>> X = ‘HI’
>> Y = ‘HE’
>> Z = strcmp( X, Y )
Z = 0
Structures
Assignment ( Variable.Property = XYZ )
>> Person(1).Name = ‘BOB’
>> Person(1).Addres = ’99 Sunset Blvd’
>> Person(1).Phone = 3334444
>> Person(2).Name = ‘JANE’
>> Person(2).Address = ‘1750 Kirby Dr’
… etc.
Cells
Assignment
>> X = { 1:4 , ‘HI’ ; [ 1 , 0 ; 0 , 1 ] , 3.1459 };
Index
>> Z = X{1,1}
-OR-
>> Z = X{1}
Z = [ 1 2 3 4 ]
>> Z = X{1,2}(1)
Z = ‘H’
>> Z = X{2,1}(1,2)
Z = 0
Nonlinear Root Finding
FZERO
( exp(1/x) sin(x)/x^2)
>> FUN = inline(‘exp(1/x) sin(x)/x^2’ );
>> X0 = 3;
>> X = fzero( FUN , X0 )
X = 3.1416
Algorithms
– Bisection Method
– Newton’s Method
ROOTS – Polynomials
( Y = -2X^3 + 4.7*X + 0.25 )
>> X = [ -2 , 0 , 4.7 , 0.25 ];
>> Z = roots( X );
Z = [1.56 -1.51 -0.05]
1
2
3
4
5
6
7
8
9
10
-150
-100
-50
0
50
100
150
y=e sin(x)x
Summary of MATLAB functions
Summary of MATLAB functions
Summary of MATLAB functions
Lecture prepared on the basis of:
http://ocw.mit.edu/OcwWeb/Mechanical-Engineering/2-
29Spring-2003/LectureNotes/ index.htm
www.owlnet.rice.edu/~ceng303/lectures/matlab/Matlab_
1.ppt
Prof. S. Boyd: Solving general linear equations using
Matlab,
www.stanford.edu/class/ee263/sle_matlab.pdf
Matlab 6.5 help
Thank you for your attention!
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