Mathematics in english

1-4 Explains the use of matrices and basic matrix operations in MATLABSolving Linear Systems of Equations p.. 1-13 Discusses the solution of simultaneous linear equations in MATLAB, incl

M ATLAB®

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MATLAB Mathematics

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Revision History:

June 2004 First printing New for MATLAB 7.0 (Release 14)

Formerly part of Using MATLAB

October 2004 Online only Revised for Version 7.0.1 (Release 14SP1)

Adding and Subtracting Matrices 1-6

Vector Products and Transpose 1-7

Multiplying Matrices 1-8

The Identity Matrix 1-10

The Kronecker Tensor Product 1-11

Vector and Matrix Norms 1-12

Solving Linear Systems of Equations 1-13

Interpolation 2-9

Interpolation Function Summary 2-9 One-Dimensional Interpolation 2-10 Two-Dimensional Interpolation 2-12 Comparing Interpolation Methods 2-13 Interpolation and Multidimensional Arrays 2-15 Triangulation and Interpolation of Scattered Data 2-18 Tessellation and Interpolation of Scattered Data in Higher Dimensions 2-26

Selected Bibliography 2-37

3

Data Analysis and Statistics

Column-Oriented Data Sets 3-3

Basic Data Analysis Functions 3-7

Function Summary 3-7 Covariance and Correlation Coefficients 3-10 Finite Differences 3-11

Data Preprocessing 3-13

The Basic Fitting Interface 3-28

Difference Equations and Filtering 3-39

Fourier Analysis and the Fast Fourier Transform (FFT) 3-42

Function Summary 3-42

Introduction 3-43

Magnitude and Phase of Transformed Data 3-47

FFT Length Versus Speed 3-49

4

Function Functions

Function Summary 4-2

Finding Zeros of Functions 4-21 Tips 4-25 Troubleshooting 4-25

Numerical Integration (Quadrature) 4-27

Example: Computing the Length of a Curve 4-27 Example: Double Integration 4-28

Parameterizing Functions Called by Function Functions 4-30

Providing Parameter Values Using Nested Functions 4-30 Providing Parameter Values to Anonymous Functions 4-31

5

Differential Equations

Initial Value Problems for ODEs and DAEs 5-2

ODE Function Summary 5-2 Introduction to Initial Value ODE Problems 5-4 Solvers for Explicit and Linearly Implicit ODEs 5-5 Examples: Solving Explicit ODE Problems 5-9 Solver for Fully Implicit ODEs 5-15 Example: Solving a Fully Implicit ODE Problem 5-16 Changing ODE Integration Properties 5-17 Examples: Applying the ODE Initial Value Problem Solvers 5-18 Questions and Answers, and Troubleshooting 5-39

Initial Value Problems for DDEs 5-45

DDE Function Summary 5-45 Introduction to Initial Value DDE Problems 5-46 DDE Solver 5-47 Solving DDE Problems 5-49 Discontinuities 5-53 Changing DDE Integration Properties 5-56

Boundary Value Problems for ODEs 5-57

BVP Function Summary 5-58

Boundary Value Problem Solver 5-60

Solving BVP Problems 5-63

Using Continuation to Make a Good Initial Guess 5-68

Solving Singular BVPs 5-75

Solving Multi-Point BVPs 5-79

Changing BVP Integration Properties 5-79

Partial Differential Equations 5-81

PDE Function Summary 5-81

Introduction to PDE Problems 5-82

MATLAB Partial Differential Equation Solver 5-83

Solving PDE Problems 5-86

Evaluating the Solution at Specific Points 5-91

Changing PDE Integration Properties 5-92

Example: Electrodynamics Problem 5-92

Sparse Matrix Storage 6-5

General Storage Information 6-6

Creating Sparse Matrices 6-7

The Bucky Ball 6-17

An Airflow Model 6-22

Sparse Matrix Operations 6-24

Computational Considerations 6-24 Standard Mathematical Operations 6-24 Permutation and Reordering 6-25 Factorization 6-29 Simultaneous Linear Equations 6-35 Eigenvalues and Singular Values 6-38

Single-Precision Mathematics 7-17

Data Type single 7-17 Single-Precision Arithmetic 7-18 The Function eps 7-19 Example — Writing M-Files for Different Data Types 7-21 Largest and Smallest Numbers of Type double and single 7-23 References 7-25

Index

Matrices and Linear

Algebra

Function Summary (p 1-2) Summarizes the MATLAB® linear algebra functions

Matrices in MATLAB (p 1-4) Explains the use of matrices and basic matrix operations

in MATLABSolving Linear Systems of Equations

(p 1-13)

Discusses the solution of simultaneous linear equations

in MATLAB, including square systems, overdetermined systems, and underdetermined systems

Inverses and Determinants (p 1-23) Explains the use in MATLAB of inverses, determinants,

and pseudoinverses in the solution of systems of linear equations

Cholesky, LU, and QR Factorizations

(p 1-28)

Discusses the solution in MATLAB of systems of linear equations that involve triangular matrices, using Cholesky factorization, Gaussian elimination, and orthogonalization

Matrix Powers and Exponentials

(p 1-35)

Explains the use of MATLAB notation to obtain various matrix powers and exponentials

1 Matrices and Linear Algebra

Function Summary

The linear algebra functions are located in the MATLAB matfun directory

Function Summary Category Function Description

Matrix analysis norm Matrix or vector norm

normest Estimate the matrix 2-norm

rank Matrix rank

trace Sum of diagonal elements

orth Orthogonalization

rref Reduced row echelon form

subspace Angle between two subspaces

Linear equations \ and / Linear equation solution

inv Matrix inverse

cond Condition number for inversion.condest 1-norm condition number estimate.chol Cholesky factorization

cholinc Incomplete Cholesky factorization.linsolve Solve a system of linear equations

lu LU factorization

luinc Incomplete LU factorization

qr Orthogonal-triangular decomposition

eig Eigenvalues and eigenvectors.

svd Singular value decomposition

eigs A few eigenvalues

svds A few singular values

poly Characteristic polynomial

polyeig Polynomial eigenvalue problem

condeig Condition number for eigenvalues

hess Hessenberg form

qz QZ factorization

schur Schur decomposition

Matrix functions expm Matrix exponential

logm Matrix logarithm

Function Summary (Continued)

Category Function Description

1 Matrices and Linear Algebra

Matrices in MATLAB

A matrix is a two-dimensional array of real or complex numbers Linear

algebra defines many matrix operations that are directly supported by

MATLAB Linear algebra includes matrix arithmetic, linear equations, eigenvalues, singular values, and matrix factorizations

For more information about creating and working with matrices, see Data Structures in the MATLAB Programming documentation

This section describes the following topics:

• “Creating Matrices” on page 1-4

• “Adding and Subtracting Matrices” on page 1-6

• “Vector Products and Transpose” on page 1-7

• “Vector Products and Transpose” on page 1-7

• “Multiplying Matrices” on page 1-8

• “The Identity Matrix” on page 1-10

• “The Kronecker Tensor Product” on page 1-11

• “Vector and Matrix Norms” on page 1-12

Creating Matrices

Informally, the terms matrix and array are often used interchangeably More precisely, a matrix is a two-dimensional rectangular array of real or complex numbers that represents a linear transformation The linear algebraic operations defined on matrices have found applications in a wide variety of technical fields (The optional Symbolic Math Toolbox extends the capabilities

of MATLAB to operations on various types of nonnumeric matrices.)MATLAB has dozens of functions that create different kinds of matrices Two

of them can be used to create a pair of 3-by-3 example matrices for use throughout this chapter The first example is symmetric:

A = pascal(3)

A =

1 1 1

1 2 3

A column vector is an m-by-1 matrix, a row vector is a 1-by-n matrix and a

scalar is a 1-by-1 matrix The statements

1 Matrices and Linear Algebra

Adding and Subtracting Matrices

Addition and subtraction of matrices is defined just as it is for arrays, element-by-element Adding A to B and then subtracting A from the result recovers B:

C = fix(10*rand(3,2))

X = A + CError using ==> +Matrix dimensions must agree

w = v + s

w =

9 7 6

Matrices in MATLAB

Vector Products and Transpose

A row vector and a column vector of the same length can be multiplied in either

order The result is either a scalar, the inner product, or a matrix, the outer

1 Matrices and Linear Algebra

If x and y are both real column vectors, the product x*y is not defined, but the two products

x'*yandy'*xare the same scalar This quantity is used so frequently, it has three different

names: inner product, scalar product, or dot product.

For a complex vector or matrix, z, the quantity z' denotes the complex

conjugate transpose, where the sign of the complex part of each element is

reversed The unconjugated complex transpose, where the complex part of each element retains its sign, is denoted by z.' So if

z = [1+2i 3+4i]

then z' is1-2i3-4iwhile z.' is1+2i3+4iFor complex vectors, the two scalar products x'*y and y'*x are complex conjugates of each other and the scalar product x'*x of a complex vector with itself is real

notation, and vector dot products:

MATLAB uses a single asterisk to denote matrix multiplication The next two

examples illustrate the fact that matrix multiplication is not commutative; AB

is usually not equal to BA:

1 Matrices and Linear Algebra

y = v*B

y =

12 -7 10Rectangular matrix multiplications must satisfy the dimension compatibility conditions:

Anything can be multiplied by a scalar:

s = 7;

w = s*v

w =

14 0 -7

The Identity Matrix

Generally accepted mathematical notation uses the capital letter to denote

identity matrices, matrices of various sizes with ones on the main diagonal and

zeros elsewhere These matrices have the property that and whenever the dimensions are compatible The original version of MATLAB could not use for this purpose because it did not distinguish between upper and lowercase letters and already served double duty as a subscript and as the complex unit So an English language pun was introduced The functioneye(m,n)

I

I

i

Matrices in MATLAB

returns an m-by-n rectangular identity matrix and eye(n) returns an n-by-n

square identity matrix

The Kronecker Tensor Product

The Kronecker product, kron(X,Y), of two matrices is the larger matrix formed from all possible products of the elements of X with those of Y If X is m-by-n and

Y is p-by-q, then kron(X,Y) is mp-by-nq The elements are arranged in the

following order:

[X(1,1)*Y X(1,2)*Y X(1,n)*Y

X(m,1)*Y X(m,2)*Y X(m,n)*Y]

The Kronecker product is often used with matrices of zeros and ones to build

up repeated copies of small matrices For example, if X is the 2-by-2 matrix

1 Matrices and Linear Algebra

Vector and Matrix Norms

The p-norm of a vector x

is computed by norm(x,p) This is defined by any value of p > 1, but the most common values of p are 1, 2, and The default value is p = 2, which

corresponds to Euclidean length:

v = [2 0 -1];

[norm(v,1) norm(v) norm(v,inf)]

ans = 3.0000 2.2361 2.0000

The p-norm of a matrix A,

can be computed for p = 1, 2, and by norm(A,p) Again, the default value is

p = 2.

C = fix(10*rand(3,2));

[norm(C,1) norm(C) norm(C,inf)]

ans = 19.0000 14.8015 13.0000

Solving Linear Systems of Equations

Solving Linear Systems of Equations

This section describes

• Computational considerations

• The general solution to a system

It also discusses particular solutions to

It is instructive to consider a 1-by-1 example

Does the equation

have a unique solution ?

The answer, of course, is yes The equation has the unique solution x = 3 The solution is easily obtained by division:

7x = 21

1 Matrices and Linear Algebra

backslash, \, are used for the two situations where the unknown matrix

appears on the left or right of the coefficient matrix:

You can think of “dividing” both sides of the equation AX = B or XA = B by A

The coefficient matrix A is always in the “denominator.”

The dimension compatibility conditions for X = A\B require the two matrices Aand B to have the same number of rows The solution X then has the same number of columns as B and its row dimension is equal to the column dimension

of A For X = B/A, the roles of rows and columns are interchanged

In practice, linear equations of the form AX = B occur more frequently than those of the form XA = B Consequently, backslash is used far more frequently

than slash The remainder of this section concentrates on the backslash operator; the corresponding properties of the slash operator can be inferred from the identity

(B/A)' = (A'\B')The coefficient matrix A need not be square If A is m-by-n, there are three

cases:

The backslash operator employs different algorithms to handle different kinds

of coefficient matrices The various cases, which are diagnosed automatically

by examining the coefficient matrix, include

• Permutations of triangular matrices

• Symmetric, positive definite matrices

• Square, nonsingular matrices

• Rectangular, overdetermined systems

X = A\B Denotes the solution to the matrix equation AX = B.

X = B/A Denotes the solution to the matrix equation XA = B.

m < n Underdetermined system Find a basic solution with at most m

nonzero components

Solving Linear Systems of Equations

General Solution

The general solution to a system of linear equations AX = b describes all

possible solutions You can find the general solution by

1 Solving the corresponding homogeneous system AX = 0 Do this using the

null command, by typing null(A) This returns a basis for the solution

space to AX = 0 Any solution is a linear combination of basis vectors.

2 Finding a particular solution to the non-homogeneous system AX = b.

You can then write any solution to AX = b as the sum of the particular solution

to AX = b, from step 2, plus a linear combination of the basis vectors from step

Nonsingular Coefficient Matrix

If the matrix A is nonsingular, the solution, x = A\b, is then the same size as

b For example,

A = pascal(3);

u = [3; 1; 4];

x = A\u

1 Matrices and Linear Algebra

If A and B are square and the same size, then X = A\B is also that size:

B = magic(3);

X = A\B

X =

19 -3 -1-17 4 13

6 0 -6

It can be confirmed that A*X is exactly equal to B.Both of these examples have exact, integer solutions This is because the coefficient matrix was chosen to be pascal(3), which has a determinant equal

to one A later section considers the effects of roundoff error inherent in more realistic computations

Singular Coefficient Matrix

A square matrix A is singular if it does not have linearly independent columns

If A is singular, the solution to AX = B either does not exist, or is not unique

The backslash operator, A\B, issues a warning if A is nearly singular and raises

an error condition if it detects exact singularity

If A is singular and AX = b has a solution, you can find a particular solution

that is not unique, by typing

1 10 18 ]

is singular, as you can verify by typing det(A)

ans =

Solving Linear Systems of Equations

Note For information about using pinv to solve systems with rectangular

coefficient matrices, see “Pseudoinverses” on page 1-24

Exact Solutions. For b =[5;2;12], the equation AX = b has an exact solution,

Least Squares Solutions. On the other hand, if b = [3;6;0], then AX = b does not

have an exact solution In this case, pinv(A)*b returns a least squares solution

If you type

A*pinv(A)*b

1 Matrices and Linear Algebra

You can determine whether AX = b has an exact solution by finding the row

reduced echelon form of the augmented matrix [A b] To do so for this example, enter

rref([A b])ans = 1.0000 0 2.2857 0

0 1.0000 1.5714 0

0 0 0 1.0000Since the bottom row contains all zeros except for the last entry, the equation does not have a solution In this case, pinv(A) returns a least-squares solution

y t ( ) c≈ 1+c2e–t

Solving Linear Systems of Equations

The preceding equation says that the vector y should be approximated by a

linear combination of two other vectors, one the constant vector containing all

ones and the other the vector with components e-t The unknown coefficients,

c1 and c2, can be computed by doing a least squares fit, which minimizes the

sum of the squares of the deviations of the data from the model There are six equations in two unknowns, represented by the 6-by-2 matrix:

In other words, the least squares fit to the data is

The following statements evaluate the model at regularly spaced increments in

t, and then plot the result, together with the original data:

y t( ) 0.4760≈ +0.3413 e–t

1 Matrices and Linear Algebra

A rectangular matrix A is rank deficient if it does not have linearly independent columns If A is rank deficient, the least squares solution to AX = B is not

unique The backslash operator, A\B, issues a warning if A is rank deficient and produces a least squares solution that has at most rank(A) nonzeros

Underdetermined Systems

Underdetermined linear systems involve more unknowns than equations When they are accompanied by additional constraints, they are the purview of

linear programming By itself, the backslash operator deals only with the

unconstrained system The solution is never unique MATLAB finds a basic solution, which has at most m nonzero components, but even this may not be

unique The particular solution actually computed is determined by the QR factorization with column pivoting (see a later section on the QR factorization)

0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9

Solving Linear Systems of Equations

Here is a small, random example:

The linear system Rx = b involves two equations in four unknowns Since the

coefficient matrix contains small integers, it is appropriate to use the format

command to display the solution in rational format The particular solution is

The complete solution to the underdetermined system can be characterized by

1 Matrices and Linear Algebra

It can be confirmed that R*Z is zero and that any vector x where

x = p + Z*qfor an arbitrary vector q satisfies R*x = b

Inverses and Determinants

Inverses and Determinants

This section provides

• An overview of the use of inverses and determinants for solving square

nonsingular systems of linear equations

• A discussion of the Moore-Penrose pseudoinverse for solving rectangular

systems of linear equations

Overview

If A is square and nonsingular, the equations AX = I and XA = I have the same solution, X This solution is called the inverse of A, is denoted by A-1, and is computed by the function inv The determinant of a matrix is useful in

theoretical considerations and some types of symbolic computation, but its scaling and roundoff error properties make it far less satisfactory for numeric computation Nevertheless, the function det computes the determinant of a square matrix:

1 Matrices and Linear Algebra

Again, because A is symmetric, has integer elements, and has determinant equal to one, so does its inverse On the other hand,

X = 0.1472 -0.1444 0.0639-0.0611 0.0222 0.1056-0.0194 0.1889 -0.1028Closer examination of the elements of X, or use of format rat, would reveal that they are integers divided by 360

If A is square and nonsingular, then without roundoff error, X = inv(A)*Bwould theoretically be the same as X = A\B and Y = B*inv(A) would theoretically be the same as Y = B/A But the computations involving the backslash and slash operators are preferable because they require less computer time, less memory, and have better error detection properties

Pseudoinverses

Rectangular matrices do not have inverses or determinants At least one of the

equations AX = I and XA = I does not have a solution A partial replacement for

Inverses and Determinants

the inverse is provided by the Moore-Penrose pseudoinverse, which is computed

by the pinv function:

1 Matrices and Linear Algebra

The solution computed by x = A\b is a basic solution; it has at most r nonzero components, where r is the rank of A The solution computed by x = pinv(A)*b

is the minimal norm solution because it minimizes norm(x) An attempt to compute a solution with x = inv(A'*A)*A'*b fails because A'*A is singular.Here is an example that illustrates the various solutions:

A = [ 1 2 3

4 5 6

7 8 9

10 11 12 ]does not have full rank Its second column is the average of the first and third columns If

b = A(:,2)

is the second column, then an obvious solution to A*x = b is x = [0 1 0]' But none of the approaches computes that x The backslash operator gives

x = A\bWarning: Rank deficient, rank = 2

x = 0.5000 0 0.5000This solution has two nonzero components The pseudoinverse approach gives

y = pinv(A)*b

y = 0.3333 0.3333 0.3333

Inverses and Determinants

There is no warning about rank deficiency But norm(y) = 0.5774 is less than norm(x) = 0.7071 Finally

1 Matrices and Linear Algebra

Cholesky, LU, and QR Factorizations

The MATLAB linear equation capabilities are based on three basic matrix factorizations:

• Cholesky factorization for symmetric, positive definite matrices

• LU factorization (Gaussian elimination) for general square matrices

• QR (orthogonal) for rectangular matrices

These three factorizations are available through the chol, lu, and qr functions

All three of these factorizations make use of triangular matrices where all the

elements either above or below the diagonal are zero Systems of linear equations involving triangular matrices are easily and quickly solved using

either forward or back substitution.

Cholesky Factorization

The Cholesky factorization expresses a symmetric matrix as the product of a triangular matrix and its transpose

where R is an upper triangular matrix.

Not all symmetric matrices can be factored in this way; the matrices that have

such a factorization are said to be positive definite This implies that all the diagonal elements of A are positive and that the offdiagonal elements are “not

too big.” The Pascal matrices provide an interesting example Throughout this chapter, the example matrix A has been the 3-by-3 Pascal matrix Temporarily switch to the 6-by-6:

Cholesky, LU, and QR Factorizations

The elements of A are binomial coefficients Each element is the sum of its

north and west neighbors The Cholesky factorization is

Note The Cholesky factorization also applies to complex matrices Any

complex matrix which has a Cholesky factorization satisfies A' = A and is said

to be Hermitian positive definite.

The Cholesky factorization allows the linear system

to be replaced by

Because the backslash operator recognizes triangular systems, this can be

solved in MATLAB quickly with

1 Matrices and Linear Algebra

LU Factorization

LU factorization, or Gaussian elimination, expresses any square matrix A as

the product of a permutation of a lower triangular matrix and an upper triangular matrix

where L is a permutation of a lower triangular matrix with ones on its diagonal and U is an upper triangular matrix.

The permutations are necessary for both theoretical and computational reasons The matrix

cannot be expressed as the product of triangular matrices without interchanging its two rows Although the matrix

can be expressed as the product of triangular matrices, when is small the elements in the factors are large and magnify errors, so even though the

permutations are not strictly necessary, they are desirable Partial pivoting ensures that the elements of L are bounded by one in magnitude and that the elements of U are not much larger than those of A.

For example[L,U] = lu(B)

L = 1.0000 0 0 0.3750 0.5441 1.0000 0.5000 1.0000 0

U = 8.0000 1.0000 6.0000