# Applied Numerical Methods Using MATLAB / Edition 1

ISBN-10: 0471698334

ISBN-13: 9780471698333

Pub. Date: 05/02/2005

Publisher: Wiley

Learn how to use MATLAB to solve complex numerical problems

Increasingly, scientists and engineers favor MATLAB over conventional programming languages such as FORTRAN and C when they wish to solve complex problems. This book will enable readers to solve problems without needing to understand all the details of the underlying theory of numerical methods.

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## Overview

Learn how to use MATLAB to solve complex numerical problems

Increasingly, scientists and engineers favor MATLAB over conventional programming languages such as FORTRAN and C when they wish to solve complex problems. This book will enable readers to solve problems without needing to understand all the details of the underlying theory of numerical methods. By providing many examples of the uses of similar functions, it guides them towards the selection of the appropriate MATLAB functions for solving their problem efficiently.

The authors have incorporated existing MATLAB functions into a series of simplified, yet complete programs that may be readily adapted by students and practitioners to solve real-world engineering and science problems. Key features include:

• More than 100 supplemental codes
• Complete MATLAB programs to demonstrate solutions to real-life
• exercises and problems
• Interactive demonstration programs that course instructors can use to produce visual presentations of the solution processes of some algorithms
• An overview of the Partial Differential Equation (PDE) toolbox
• An appendix with MATLAB commands/functions for symbolic computation

With very little prior programming experience, students and practitioners will find this approach invaluable to quickly learn how to solve their numerical problems.

## Product Details

ISBN-13:
9780471698333
Publisher:
Wiley
Publication date:
05/02/2005
Edition description:
New Edition
Pages:
528
Product dimensions:
6.42(w) x 9.37(h) x 1.20(d)

## Related Subjects

Preface xiii

1 MATLAB Usage and Computational Errors 1

1.1 Basic Operations of MATLAB  1

1.1.1 Input/Output of Data from MATLAB Command Window 2

1.1.2 Input/Output of Data Through Files 2

1.1.3 Input/Output of Data Using Keyboard 4

1.1.4 2-D Graphic Input/Output 5

1.1.5 3-D Graphic Output 10

1.1.6 Mathematical Functions 10

1.1.7 Operations on Vectors and Matrices 15

1.1.8 Random Number Generators 22

1.1.9 Flow Control 24

1.2 Computer Errors Versus Human Mistakes 27

1.2.1 IEEE 64-bit Floating-Point Number Representation 28

1.2.2 Various Kinds of Computing Errors 31

1.2.3 Absolute/Relative Computing Errors 33

1.2.4 Error Propagation 33

1.2.5 Tips for Avoiding Large Errors 34

1.3 Toward Good Program 37

1.3.1 Nested Computing for Computational Efficiency 37

1.3.2 Vector Operation Versus Loop Iteration 39

1.3.3 Iterative Routine Versus Nested Routine 40

1.3.4 To Avoid Runtime Error 40

1.3.5 Parameter Sharing via Global Variables 44

1.3.6 Parameter Passing Through Varargin 45

1.3.7 Adaptive Input Argument List 46

Problems 46

2 System of Linear Equations 71

2.1 Solution for a System of Linear Equations 72

2.1.1 The Nonsingular Case (M = N) 72

2.1.2 The Underdetermined Case (M < N): Minimum-Norm Solution 72

2.1.3 The Overdetermined Case (M >N): Least-Squares Error Solution 75

2.1.4 RLSE (Recursive Least-Squares Estimation) 76

2.2 Solving a System of Linear Equations 79

2.2.1 Gauss Elimination 79

2.2.2 Partial Pivoting 81

2.2.3 Gauss–Jordan Elimination 89

2.3 Inverse Matrix 92

2.4 Decomposition (Factorization) 92

2.4.1 LU Decomposition (Factorization): Triangularization 92

2.4.2 Other Decomposition (Factorization): Cholesky, QR, and SVD 97

2.5 Iterative Methods to Solve Equations 98

2.5.1 Jacobi Iteration 98

2.5.2 Gauss–Seidel Iteration 100

2.5.3 The Convergence of Jacobi and Gauss–Seidel Iterations 103

Problems 104

3 Interpolation and Curve Fitting 117

3.1 Interpolation by Lagrange Polynomial 117

3.2 Interpolation by Newton Polynomial 119

3.3 Approximation by Chebyshev Polynomial 124

3.4 Pade Approximation by Rational Function 129

3.5 Interpolation by Cubic Spline 133

3.6 Hermite Interpolating Polynomial 139

3.7 Two-dimensional Interpolation 141

3.8 Curve Fitting 143

3.8.1 Straight Line Fit: A Polynomial Function of First Degree 144

3.8.2 Polynomial Curve Fit: A Polynomial Function of Higher Degree 145

3.8.3 Exponential Curve Fit and Other Functions 149

3.9 Fourier Transform 150

3.9.1 FFT Versus DFT 151

3.9.2 Physical Meaning of DFT 152

3.9.3 Interpolation by Using DFS 155

Problems 157

4 Nonlinear Equations 179

4.1 Iterative Method Toward Fixed Point 179

4.2 Bisection Method 183

4.3 False Position or Regula Falsi Method 185

4.4 Newton(–Raphson) Method  186

4.5 Secant Method 189

4.6 Newton Method for a System of Nonlinear Equations 191

4.7 Symbolic Solution for Equations 193

4.8 A Real-World Problem 194

Problems 197

5 Numerical Differentiation/Integration 209

5.1 Difference Approximation for First Derivative 209

5.2 Approximation Error of First Derivative 211

5.3 Difference Approximation for Second and Higher Derivative 216

5.4 Interpolating Polynomial and Numerical Differential 220

5.5 Numerical Integration and Quadrature 222

5.6 Trapezoidal Method and Simpson Method 226

5.7 Recursive Rule and Romberg Integration 228

5.9.1 Gauss–Legendre Integration 235

5.9.2 Gauss–Hermite Integration 238

5.9.3 Gauss–Laguerre Integration 239

5.9.4 Gauss–Chebyshev Integration 240

5.10 Double Integral 241

Problems 244

6 Ordinary Differential Equations 263

6.1 Euler’s Method 263

6.2 Heun’s Method: Trapezoidal Method 266

6.3 Runge–Kutta Method 267

6.4 Predictor–Corrector Method 269

6.4.2 Hamming Method 273

6.4.3 Comparison of Methods 274

6.5 Vector Differential Equations 277

6.5.1 State Equation 277

6.5.2 Discretization of LTI State Equation 281

6.5.3 High-Order Differential Equation to State Equation 283

6.5.4 Stiff Equation 284

6.6 Boundary Value Problem (BVP) 287

6.6.1 Shooting Method 287

6.6.2 Finite Difference Method 290

Problems 293

7 Optimization 321

7.1 Unconstrained Optimization [L-2, Chapter 7] 321

7.1.1 Golden Search Method 321

7.1.4 Steepest Descent Method 328

7.1.5 Newton Method 330

7.1.7 Simulated Annealing Method [W-7] 334

7.1.8 Genetic Algorithm [W-7] 338

7.2 Constrained Optimization [L-2, Chapter 10] 343

7.2.1 Lagrange Multiplier Method 343

7.2.2 Penalty Function Method 346

7.3 MATLAB Built-In Routines for Optimization 350

7.3.1 Unconstrained Optimization 350

7.3.2 Constrained Optimization 352

7.3.3 Linear Programming (LP) 355

Problems 357

8 Matrices and Eigenvalues 371

8.1 Eigenvalues and Eigenvectors 371

8.2 Similarity Transformation and Diagonalization 373

8.3 Power Method 378

8.3.1 Scaled Power Method 378

8.3.2 Inverse Power Method 380

8.3.3 Shifted Inverse Power Method 380

8.4 Jacobi Method 381

8.5 Physical Meaning of Eigenvalues/Eigenvectors 385

8.6 Eigenvalue Equations 389

Problems 390

9 Partial Differential Equations 401

9.1 Elliptic PDE 402

9.2 Parabolic PDE 406

9.2.1 The Explicit Forward Euler Method 406

9.2.2 The Implicit Backward Euler Method 407

9.2.3 The Crank–Nicholson Method 409

9.2.4 Two-Dimensional Parabolic PDE 412

9.3 Hyperbolic PDE 414

9.3.1 The Explicit Central Difference Method 415

9.3.2 Two-Dimensional Hyperbolic PDE 417

9.4 Finite Element Method (FEM) for solving PDE 420

9.5 GUI of MATLAB for Solving PDEs: PDETOOL 429

9.5.1 Basic PDEs Solvable by PDETOOL 430

9.5.2 The Usage of PDETOOL 431

9.5.3 Examples of Using PDETOOL to Solve PDEs 435

Problems 444

Appendix A. Mean Value Theorem 461

Appendix B. Matrix Operations/Properties 463

Appendix C. Differentiation with Respect to a Vector 471

Appendix D. Laplace Transform 473

Appendix E. Fourier Transform 475

Appendix F. Useful Formulas 477

Appendix G. Symbolic Computation 481

Appendix H. Sparse Matrices 489

Appendix I. MATLAB 491

References 497

Subject Index 499

Index for MATLAB Routines 503

Index for Tables 509