【新书推荐】【2020.05】基于MATLAB的应用数值方法（第二版）

这个新版本为学生、工程师和研究人员提供了更新的方法，通过用MATLAB®应用数值方法解决问题

This new edition provides an updated approach for students, engineers, and researchers to apply numerical methods for solving problems using MATLAB®

这本书利用MATLAB®软件教授应用数值方法解决实际工程和/或科学问题的基本概念。它以一种完整的形式呈现程序，这样，读者就可以在没有编程技巧的情况下立即运行程序，使他们能够集中精力理解数学运算过程并对结果进行解释。

This accessible book makes use of MATLAB® software to teach the fundamental concepts for applying numerical methods to solve practical engineering and/or science problems. It presents programs in a complete form so that readers can run them instantly with no programming skill, allowing them to focus on understanding the mathematical manipulation process and making interpretations of the results.

本书首先介绍了MATLAB的使用和计算误差，涵盖了从数据输入/输出到各种计算误差，以及参数共享和传递等所有方面。接下来介绍了线性方程组，然后是关于拉格朗日多项式插值的一章。下一节介绍插值和曲线拟合、非线性方程、数值微分/积分、常微分方程和优化。许多方法，如辛普森、欧拉、休恩、龙格库塔、黄金搜索、内尔德米德等等都涵盖在这些章节。第八章为读者提供了矩阵、特征值和特征向量等资源。这本书以微分方程的完整概述而结束。

Applied Numerical Methods Using MATLAB®, Second Edition begins with an introduction to MATLAB usage and computational errors, covering everything from input/output of data, to various kinds of computing errors, and on to parameter sharing and passing, and more. The system of linear equations is covered next, followed by a chapter on the interpolation by Lagrange polynomial. The next sections look at interpolation and curve fitting, nonlinear equations, numerical differentiation/integration, ordinary differential equations, and optimization. Numerous methods such as the Simpson, Euler, Heun, Runge-kutta, Golden Search, Nelder-Mead, and more are all covered in those chapters. The eighth chapter provides readers with matrices and Eigenvalues and Eigenvectors. The book finishes with a complete overview of differential equations.

提供解决电子电路和神经网络的例子和问题

包括自适应滤波器、递推最小二乘估计、多项式方程的Bairstow方法等新章节

说明了混合整数线性规划（MILP）和DOA（到达方向）估计与特征向量

针对不喜欢和/或没有时间推导和证明数学结果的学生

本书是一个很好的文本，为学生提高他们的问题解决能力，而不涉及详细的MATLAB代码。对于那些想深入了解底层算法和公式的人来说，它也很有用。

Provides examples and problems of solving electronic circuits and neural networks

Includes new sections on adaptive filters, recursive least-squares estimation, Bairstow’s method for a polynomial equation, and more

Explains Mixed Integer Linear Programing (MILP) and DOA (Direction of Arrival) estimation with eigenvectors

Aimed at students who do not like and/or do not have time to derive and prove mathematical results

Applied Numerical Methods Using MATLAB®, Second Edition is an excellent text for students who wish to develop their problem-solving capability without being involved in details about the MATLAB codes. It will also be useful to those who want to delve deeper into understanding underlying algorithms and equations.

TABLE OF CONTENTS
Preface xv

Acknowledgments xvii

About the Companion Website xix

1 MATLAB Usage and Computational Errors 1

1.1 Basic Operations of MATLAB 2

1.1.1 Input/Output of Data from MATLAB Command Window 3

1.1.2 Input/Output of Data Through Files 3

1.1.3 Input/Output of Data Using Keyboard 5

1.1.4 Two-Dimensional (2D) Graphic Input/Output 6

1.1.5 Three Dimensional (3D) Graphic Output 12

1.1.6 Mathematical Functions 13

1.1.7 Operations on Vectors and Matrices 16

1.1.8 Random Number Generators 25

1.1.9 Flow Control 27

1.2 Computer Errors vs. Human Mistakes 31

1.2.1 IEEE 64-bit Floating-Point Number Representation 31

1.2.2 Various Kinds of Computing Errors 35

1.2.3 Absolute/Relative Computing Errors 37

1.2.4 Error Propagation 38

1.2.5 Tips for Avoiding Large Errors 39

1.3 Toward Good Program 42

1.3.1 Nested Computing for Computational Efficiency 42

1.3.2 Vector Operation vs. Loop Iteration 43

1.3.3 Iterative Routine vs. Recursive Routine 45

1.3.4 To Avoid Runtime Error 45

1.3.5 Parameter Sharing via GLOBAL Variables 49

1.3.6 Parameter Passing Through VARARGIN 50

1.3.7 Adaptive Input Argument List 51

Problems 52

2 System of Linear Equations 77

2.1 Solution for a System of Linear Equations 78

2.1.1 The Nonsingular Case (M = N) 78

2.1.2 The Underdetermined Case (M < N): Minimum-norm Solution 79

2.1.3 The Overdetermined Case (M > N): Least-squares Error Solution 82

2.1.4 Recursive Least-Squares Estimation (RLSE) 83

2.2 Solving a System of Linear Equations 86

2.2.1 Gauss(ian) Elimination 86

2.2.2 Partial Pivoting 88

2.2.3 Gauss-Jordan Elimination 97

2.3 Inverse Matrix 100

2.4 Decomposition (Factorization) 100

2.4.1 LU Decomposition (Factorization) – Triangularization 100

2.4.2 Other Decomposition (Factorization) – Cholesky, QR and SVD 105

2.5 Iterative Methods to Solve Equations 108

2.5.1 Jacobi Iteration 108

2.5.2 Gauss-Seidel Iteration 111

2.5.3 The Convergence of Jacobi and Gauss-Seidel Iterations 115

Problems 117

3 Interpolation and Curve Fitting 129

3.1 Interpolation by Lagrange Polynomial 130

3.2 Interpolation by Newton Polynomial 132

3.3 Approximation by Chebyshev Polynomial 137

3.4 Pade Approximation by Rational Function 142

3.5 Interpolation by Cubic Spline 146

3.6 Hermite Interpolating Polynomial 153

3.7 Two-Dimensional Interpolation 155

3.8 Curve Fitting 158

3.8.1 Straight-Line Fit – A Polynomial Function of Degree 1 158

3.8.2 Polynomial Curve Fit – A Polynomial Function of Higher Degree 160

3.8.3 Exponential Curve Fit and Other Functions 165

3.9 Fourier Transform 166

3.9.1 FFT vs. DFT 167

3.9.2 Physical Meaning of DFT 169

3.9.3 Interpolation by Using DFS 172

Problems 175

4 Nonlinear Equations 197

4.1 Iterative Method toward Fixed Point 197

4.2 Bisection Method 201

4.3 False Position or Regula Falsi Method 203

4.4 Newton(-Raphson) Method 205

4.5 Secant Method 208

4.6 Newton Method for a System of Nonlinear Equations 209

4.7 Bairstow’s Method for a Polynomial Equation 212

4.8 Symbolic Solution for Equations 215

4.9 Real-World Problems 216

Problems 223

5 Numerical Differentiation/Integration 245

5.1 Difference Approximation for the First Derivative 246

5.2 Approximation Error of the First Derivative 248

5.3 Difference Approximation for Second and Higher Derivative 253

5.4 Interpolating Polynomial and Numerical Differential 258

5.5 Numerical Integration and Quadrature 259

5.6 Trapezoidal Method and Simpson Method 263

5.7 Recursive Rule and Romberg Integration 265

5.8 Adaptive Quadrature 268

5.9 Gauss Quadrature 272

5.9.1 Gauss-Legendre Integration 272

5.9.2 Gauss-Hermite Integration 275

5.9.3 Gauss-Laguerre Integration 277

5.9.4 Gauss-Chebyshev Integration 277

5.10 Double Integral 278

5.11 Integration Involving PWL Function 281

Problems 285

6 Ordinary Differential Equations 305

6.1 Euler’s Method 306

6.2 Heun’s Method – Trapezoidal Method 309

6.3 Runge-Kutta Method 310

6.4 Predictor-Corrector Method 312

6.4.1 Adams-Bashforth-Moulton Method 312

6.4.2 Hamming Method 316

6.4.3 Comparison of Methods 317

6.5 Vector Differential Equations 320

6.5.1 State Equation 320

6.5.2 Discretization of LTI State Equation 324

6.5.3 High-order Differential Equation to State Equation 327

6.5.4 Stiff Equation 328

6.6 Boundary Value Problem (BVP) 333

6.6.1 Shooting Method 333

6.6.2 Finite Difference Method 336

Problems 341

7 Optimization 375

7.1 Unconstrained Optimization 376

7.1.1 Golden Search Method 376

7.1.2 Quadratic Approximation Method 378

7.1.3 Nelder-Mead Method 380

7.1.4 Steepest Descent Method 383

7.1.5 Newton Method 385

7.1.6 Conjugate Gradient Method 387

7.1.7 Simulated Annealing 389

7.1.8 Genetic Algorithm 393

7.2 Constrained Optimization 399

7.2.1 Lagrange Multiplier Method 399

7.2.2 Penalty Function Method 406

7.3 MATLAB Built-In Functions for Optimization 409

7.3.1 Unconstrained Optimization 409

7.3.2 Constrained Optimization 413

7.3.3 Linear Programming (LP) 416

7.3.4 Mixed Integer Linear Programming (MILP) 423

7.4 Neural Network[K-1] 433

7.5 Adaptive Filter[Y-3] 439

7.6 Recursive Least Square Estimation (RLSE)[Y-3] 443

Problems 448

8 Matrices and Eigenvalues 467

8.1 Eigenvalues and Eigenvectors 468

8.2 Similarity Transformation and Diagonalization 469

8.3 Power Method 475

8.3.1 Scaled Power Method 475

8.3.2 Inverse Power Method 476

8.3.3 Shifted Inverse Power Method 477

8.4 Jacobi Method 478

8.5 Gram-Schmidt Orthonormalization and QR Decomposition 481

8.6 Physical Meaning of Eigenvalues/Eigenvectors 485

8.7 Differential Equations with Eigenvectors 489

8.8 DoA Estimation with Eigenvectors[Y-3] 493

Problems 499

9 Partial Differential Equations 509

9.1 Elliptic PDE 510

9.2 Parabolic PDE 515

9.2.1 The Explicit Forward Euler Method 515

9.2.2 The Implicit Backward Euler Method 516

9.2.3 The Crank-Nicholson Method 518

9.2.4 Using the MATLAB function ‘pdepe()’ 520

9.2.5 Two-Dimensional Parabolic PDEs 523

9.3 Hyperbolic PDES 526

9.3.1 The Explicit Central Difference Method 526

9.3.2 Two-Dimensional Hyperbolic PDEs 529

9.4 Finite Element Method (FEM) for Solving PDE 532

9.5 GUI of MATLAB for Solving PDES – PDE tool 543

9.5.1 Basic PDEs Solvable by PDEtool 543

9.5.2 The Usage of PDEtool 545

9.5.3 Examples of Using PDEtool to Solve PDEs 549

Problems 559

Appendix A Mean Value Theorem 575

Appendix B Matrix Operations/Properties 577

B.1 Addition and Subtraction 578

B.2 Multiplication 578

B.3 Determinant 578

B.4 Eigenvalues and Eigenvectors of a Matrix 579

B.5 Inverse Matrix 580

B.6 Symmetric/Hermitian Matrix 580

B.7 Orthogonal/Unitary Matrix 581

B.8 Permutation Matrix 581

B.9 Rank 581

B.10 Row Space and Null Space 581

B.11 Row Echelon Form 582

B.12 Positive Definiteness 582

B.13 Scalar (Dot) Product and Vector (Cross) Product 583

B.14 Matrix Inversion Lemma 584

Appendix C Differentiation W.R.T. A Vector 585

Appendix D Laplace Transform 587

Appendix E Fourier Transform 589

Appendix F Useful Formulas 591

Appendix G Symbolic Computation 595

G.1 How to Declare Symbolic Variables and Handle Symbolic Expressions 595

G.2 Calculus 597

G.2.1 Symbolic Summation 597

G.2.2 Limits 597

G.2.3 Differentiation 598

G.2.4 Integration 598

G.2.5 Taylor Series Expansion 599

G.3 Linear Algebra 600

G.4 Solving Algebraic Equations 601

G.5 Solving Differential Equations 601

Appendix H Sparse Matrices 603

Appendix I MATLAB 605

References 611

Index 613

Index for MATLAB Functions 619

Index for Tables 629

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