ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and...

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ES 240: Scientific and Engineering Computation. Chap Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University

Transcript of ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and...

Page 1: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Chapter 8: Linear Algebraic Equations and Matrices

Uchechukwu Ofoegbu

Temple University

Page 2: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

OverviewOverview

Matrix:– rectangular array of elements represented by a single

symbol (e.g. [A]).

Element– An individual entry of a matrix

– example: a23 – arow column

Page 3: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Overview (cont)Overview (cont)

A horizontal set of elements is called a row and a vertical set of elements is called a column.

The first subscript of an element indicates the row while the second indicates the column.

The size of a matrix is given as m rows by n columns, or simply m by n (or m x n).

1 x n matrices are row vectors.

m x 1 matrices are column vectors.

Page 4: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Special MatricesSpecial Matrices

Matrices where m=n are called square matrices. There are a number of special forms of square matrices:

Symmetric

A 5 1 2

1 3 7

2 7 8

Diagonal

A a11

a22

a33

Identity

A 1

1

1

Upper Triangular

A a11 a12 a13

a22 a23

a33

Lower Triangular

A a11

a21 a22

a31 a32 a33

Banded

A

a11 a12

a21 a22 a23

a32 a33 a34

a43 a44

Page 5: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Matrix OperationsMatrix Operations

Equal Matrices– Two matrices are considered equal if and only if every element in the first

matrix is equal to every corresponding element in the second. – Both matrices must be the same size.

Matrix addition and subtraction– performed by adding or subtracting the corresponding elements. – Matrices must be the same size.

Page 6: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Example Addition & SubtractionExample Addition & Subtraction

2 1 3 1 4 7

4 0 5 8 3 2

1 3 10

12 3 3

1 82 1 3

4 34 0 5

7 2

2 1 3 1 4 7

4 0 5 8 3 2

3 5 4

4 3 7

is not defined.

Page 7: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Matrix MultiplicationMatrix Multiplication

Scalar matrix multiplication is performed by multiplying each element by the same scalar.

If A is a row matrix and B is a column matrix, then we can form the product AB provided that the two matrices have the same length.

The product AB is a 1x1 matrix obtained by multiplying corresponding entries of A and B and then forming the sum.

1

21 2 1 1 2 2n n n

n

b

ba a a a b a b a b

b

Page 8: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Example Multiplying Row to ColumnExample Multiplying Row to Column

3

2 1 3 2

5

2 3 1 2 3 5 7

3

4 0 2 1 2

5

is not defined.

Page 9: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Matrix MultiplicationMatrix Multiplication

If A is an mxn matrix and B is an nxq matrix, then we can form the product AB.

The product AB is an mxq matrix whose entries are obtained by multiplying the rows of A by the columns of B.

The entry in the ith row and jth column of the product AB is formed by multiplying the ith row of A and jth column of B.

c ij aikbkj

k1

n

Page 10: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Example Matrix MultiplicationExample Matrix Multiplication

7 12 -5

-19 0 2

3 2 02 1 3

2 1 23 0 2

5 3 1

is not defined.

3 2 02 1 3

2 1 23 0 2

5 3 1

Matlab command: A*B Matlab command: A*B – no dot multiplication– no dot multiplication

Page 11: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Matrix Inverse and TransposeMatrix Inverse and Transpose

The inverse of a square matrix A, denoted by A-1, is a square matrix with the property

A-1A = AA-1 = I,where I is an identity matrix of the same size. – Matlab command: inv(A), A^-1

The transpose of a matrix involves transforming its rows into columns and its columns into rows.

– (aij)T=aji– Matlab command: a’ or transpose(a)

Page 12: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Example Example

Verify that is the inverse of 4 111 11

3 211 11

2 1.

3 4

4 1 2 1 1 011 113 3 4 0 1211 11

4 12 1 1 011 1133 4 0 1211 11

checks

checks

Page 13: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Representing Linear AlgebraRepresenting Linear Algebra

Matrices provide a concise notation for representing and solving simultaneous linear equations:

a11x1 a12x2 a13x3 b1

a21x1 a22x2 a23x3 b2

a31x1 a32x2 a33x3 b3

a11 a12 a13

a21 a22 a23

a31 a32 a33

x1

x2

x3

b1

b2

b3

[A]{x} {b}

Page 14: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Solving a Matrix EquationSolving a Matrix Equation

Solving a Matrix Equation – If the matrix A has an inverse, then the solution of the matrix equation

AX = B is given by X = A-1B.

Page 15: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Example Solving a Matrix EquationExample Solving a Matrix Equation

Use a matrix equation to solve 2 4 2

3 7 7.

x y

x y

The matrix form of the equation is

2 4 2.

3 7 7

x

y

1 7 22 4 2 2 72

3 7 7 3 7 412

x

y

Page 16: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

Solving With MATLABSolving With MATLAB

MATLAB provides two direct ways to solve systems of linear algebraic equations [A]{x}={b}:– Left-divisionx = A\b

– Matrix inversionx = inv(A)*b

Disadvantages of the matrix inverse method:– less efficient than left-division – only works for square, non-singular systems.

Page 17: ES 240: Scientific and Engineering Computation. Chapter 8 Chapter 8: Linear Algebraic Equations and Matrices Uchechukwu Ofoegbu Temple University.

ES 240: Scientific and Engineering Computation. Chapter 8

LabLab

Ex 8.9 (see pgs 193/194)