{"product_id":"applied-numerical-methods-using-matlab-isbn-9781119626800","title":"Applied Numerical Methods Using MATLAB","description":"\u003cp\u003e\u003cb\u003eThis new edition provides an updated approach for students, engineers, and researchers to apply numerical methods for solving problems using MATLAB\u003c\/b\u003e\u003csup\u003e®\u003c\/sup\u003e \u003c\/p\u003e \u003cp\u003eThis accessible book makes use of MATLAB\u003csup\u003e®\u003c\/sup\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eApplied Numerical Methods Using MATLAB\u003csup\u003e®\u003c\/sup\u003e, Second Edition\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides examples and problems of solving electronic circuits and neural networks\u003c\/li\u003e \u003cli\u003eIncludes new sections on adaptive filters, recursive least-squares estimation, Bairstow's method for a polynomial equation, and more\u003c\/li\u003e \u003cli\u003eExplains Mixed Integer Linear Programing (MILP) and DOA (Direction of Arrival) estimation with eigenvectors\u003c\/li\u003e \u003cli\u003eAimed at students who do not like and\/or do not have time to derive and prove mathematical results\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Numerical Methods Using MATLAB\u003csup\u003e®\u003c\/sup\u003e, Second Edition\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xvii\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 MATLAB Usage and Computational Errors 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Basic Operations of MATLAB 2\u003c\/p\u003e \u003cp\u003e1.1.1 Input\/Output of Data from MATLAB Command Window 3\u003c\/p\u003e \u003cp\u003e1.1.2 Input\/Output of Data Through Files 3\u003c\/p\u003e \u003cp\u003e1.1.3 Input\/Output of Data Using Keyboard 5\u003c\/p\u003e \u003cp\u003e1.1.4 Two-Dimensional (2D) Graphic Input\/Output 6\u003c\/p\u003e \u003cp\u003e1.1.5 Three Dimensional (3D) Graphic Output 12\u003c\/p\u003e \u003cp\u003e1.1.6 Mathematical Functions 13\u003c\/p\u003e \u003cp\u003e1.1.7 Operations on Vectors and Matrices 16\u003c\/p\u003e \u003cp\u003e1.1.8 Random Number Generators 25\u003c\/p\u003e \u003cp\u003e1.1.9 Flow Control 27\u003c\/p\u003e \u003cp\u003e1.2 Computer Errors vs. Human Mistakes 31\u003c\/p\u003e \u003cp\u003e1.2.1 IEEE 64-bit Floating-Point Number Representation 31\u003c\/p\u003e \u003cp\u003e1.2.2 Various Kinds of Computing Errors 35\u003c\/p\u003e \u003cp\u003e1.2.3 Absolute\/Relative Computing Errors 37\u003c\/p\u003e \u003cp\u003e1.2.4 Error Propagation 38\u003c\/p\u003e \u003cp\u003e1.2.5 Tips for Avoiding Large Errors 39\u003c\/p\u003e \u003cp\u003e1.3 Toward Good Program 42\u003c\/p\u003e \u003cp\u003e1.3.1 Nested Computing for Computational Efficiency 42\u003c\/p\u003e \u003cp\u003e1.3.2 Vector Operation vs. Loop Iteration 43\u003c\/p\u003e \u003cp\u003e1.3.3 Iterative Routine vs. Recursive Routine 45\u003c\/p\u003e \u003cp\u003e1.3.4 To Avoid Runtime Error 45\u003c\/p\u003e \u003cp\u003e1.3.5 Parameter Sharing via GLOBAL Variables 49\u003c\/p\u003e \u003cp\u003e1.3.6 Parameter Passing Through VARARGIN 50\u003c\/p\u003e \u003cp\u003e1.3.7 Adaptive Input Argument List 51\u003c\/p\u003e \u003cp\u003eProblems 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 System of Linear Equations 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Solution for a System of Linear Equations 78\u003c\/p\u003e \u003cp\u003e2.1.1 The Nonsingular Case (\u003ci\u003eM \u003c\/i\u003e= \u003ci\u003eN\u003c\/i\u003e) 78\u003c\/p\u003e \u003cp\u003e2.1.2 The Underdetermined Case (\u003ci\u003eM \u0026lt; N\u003c\/i\u003e): Minimum-norm Solution 79\u003c\/p\u003e \u003cp\u003e2.1.3 The Overdetermined Case (\u003ci\u003eM \u0026gt; N\u003c\/i\u003e): Least-squares Error Solution 82\u003c\/p\u003e \u003cp\u003e2.1.4 Recursive Least-Squares Estimation (RLSE) 83\u003c\/p\u003e \u003cp\u003e2.2 Solving a System of Linear Equations 86\u003c\/p\u003e \u003cp\u003e2.2.1 Gauss(ian) Elimination 86\u003c\/p\u003e \u003cp\u003e2.2.2 Partial Pivoting 88\u003c\/p\u003e \u003cp\u003e2.2.3 Gauss-Jordan Elimination 97\u003c\/p\u003e \u003cp\u003e2.3 Inverse Matrix 100\u003c\/p\u003e \u003cp\u003e2.4 Decomposition (Factorization) 100\u003c\/p\u003e \u003cp\u003e2.4.1 \u003ci\u003eLU \u003c\/i\u003eDecomposition (Factorization) – Triangularization 100\u003c\/p\u003e \u003cp\u003e2.4.2 Other Decomposition (Factorization) – Cholesky, \u003ci\u003eQR \u003c\/i\u003eand SVD 105\u003c\/p\u003e \u003cp\u003e2.5 Iterative Methods to Solve Equations 108\u003c\/p\u003e \u003cp\u003e2.5.1 Jacobi Iteration 108\u003c\/p\u003e \u003cp\u003e2.5.2 Gauss-Seidel Iteration 111\u003c\/p\u003e \u003cp\u003e2.5.3 The Convergence of Jacobi and Gauss-Seidel Iterations 115\u003c\/p\u003e \u003cp\u003eProblems 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Interpolation and Curve Fitting 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Interpolation by Lagrange Polynomial 130\u003c\/p\u003e \u003cp\u003e3.2 Interpolation by Newton Polynomial 132\u003c\/p\u003e \u003cp\u003e3.3 Approximation by Chebyshev Polynomial 137\u003c\/p\u003e \u003cp\u003e3.4 Pade Approximation by Rational Function 142\u003c\/p\u003e \u003cp\u003e3.5 Interpolation by Cubic Spline 146\u003c\/p\u003e \u003cp\u003e3.6 Hermite Interpolating Polynomial 153\u003c\/p\u003e \u003cp\u003e3.7 Two-Dimensional Interpolation 155\u003c\/p\u003e \u003cp\u003e3.8 Curve Fitting 158\u003c\/p\u003e \u003cp\u003e3.8.1 Straight-Line Fit – A Polynomial Function of Degree 1 158\u003c\/p\u003e \u003cp\u003e3.8.2 Polynomial Curve Fit – A Polynomial Function of Higher Degree 160\u003c\/p\u003e \u003cp\u003e3.8.3 Exponential Curve Fit and Other Functions 165\u003c\/p\u003e \u003cp\u003e3.9 Fourier Transform 166\u003c\/p\u003e \u003cp\u003e3.9.1 FFT vs. DFT 167\u003c\/p\u003e \u003cp\u003e3.9.2 Physical Meaning of DFT 169\u003c\/p\u003e \u003cp\u003e3.9.3 Interpolation by Using DFS 172\u003c\/p\u003e \u003cp\u003eProblems 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Nonlinear Equations 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Iterative Method toward Fixed Point 197\u003c\/p\u003e \u003cp\u003e4.2 Bisection Method 201\u003c\/p\u003e \u003cp\u003e4.3 False Position or Regula Falsi Method 203\u003c\/p\u003e \u003cp\u003e4.4 Newton(-Raphson) Method 205\u003c\/p\u003e \u003cp\u003e4.5 Secant Method 208\u003c\/p\u003e \u003cp\u003e4.6 Newton Method for a System of Nonlinear Equations 209\u003c\/p\u003e \u003cp\u003e4.7 Bairstow’s Method for a Polynomial Equation 212\u003c\/p\u003e \u003cp\u003e4.8 Symbolic Solution for Equations 215\u003c\/p\u003e \u003cp\u003e4.9 Real-World Problems 216\u003c\/p\u003e \u003cp\u003eProblems 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Numerical Differentiation\/Integration 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Difference Approximation for the First Derivative 246\u003c\/p\u003e \u003cp\u003e5.2 Approximation Error of the First Derivative 248\u003c\/p\u003e \u003cp\u003e5.3 Difference Approximation for Second and Higher Derivative 253\u003c\/p\u003e \u003cp\u003e5.4 Interpolating Polynomial and Numerical Differential 258\u003c\/p\u003e \u003cp\u003e5.5 Numerical Integration and Quadrature 259\u003c\/p\u003e \u003cp\u003e5.6 Trapezoidal Method and Simpson Method 263\u003c\/p\u003e \u003cp\u003e5.7 Recursive Rule and Romberg Integration 265\u003c\/p\u003e \u003cp\u003e5.8 Adaptive Quadrature 268\u003c\/p\u003e \u003cp\u003e5.9 Gauss Quadrature 272\u003c\/p\u003e \u003cp\u003e5.9.1 Gauss-Legendre Integration 272\u003c\/p\u003e \u003cp\u003e5.9.2 Gauss-Hermite Integration 275\u003c\/p\u003e \u003cp\u003e5.9.3 Gauss-Laguerre Integration 277\u003c\/p\u003e \u003cp\u003e5.9.4 Gauss-Chebyshev Integration 277\u003c\/p\u003e \u003cp\u003e5.10 Double Integral 278\u003c\/p\u003e \u003cp\u003e5.11 Integration Involving PWL Function 281\u003c\/p\u003e \u003cp\u003eProblems 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Ordinary Differential Equations 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Euler’s Method 306\u003c\/p\u003e \u003cp\u003e6.2 Heun’s Method – Trapezoidal Method 309\u003c\/p\u003e \u003cp\u003e6.3 Runge-Kutta Method 310\u003c\/p\u003e \u003cp\u003e6.4 Predictor-Corrector Method 312\u003c\/p\u003e \u003cp\u003e6.4.1 Adams-Bashforth-Moulton Method 312\u003c\/p\u003e \u003cp\u003e6.4.2 Hamming Method 316\u003c\/p\u003e \u003cp\u003e6.4.3 Comparison of Methods 317\u003c\/p\u003e \u003cp\u003e6.5 Vector Differential Equations 320\u003c\/p\u003e \u003cp\u003e6.5.1 State Equation 320\u003c\/p\u003e \u003cp\u003e6.5.2 Discretization of LTI State Equation 324\u003c\/p\u003e \u003cp\u003e6.5.3 High-order Differential Equation to State Equation 327\u003c\/p\u003e \u003cp\u003e6.5.4 Stiff Equation 328\u003c\/p\u003e \u003cp\u003e6.6 Boundary Value Problem (BVP) 333\u003c\/p\u003e \u003cp\u003e6.6.1 Shooting Method 333\u003c\/p\u003e \u003cp\u003e6.6.2 Finite Difference Method 336\u003c\/p\u003e \u003cp\u003eProblems 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Optimization 375\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Unconstrained Optimization 376\u003c\/p\u003e \u003cp\u003e7.1.1 Golden Search Method 376\u003c\/p\u003e \u003cp\u003e7.1.2 Quadratic Approximation Method 378\u003c\/p\u003e \u003cp\u003e7.1.3 Nelder-Mead Method 380\u003c\/p\u003e \u003cp\u003e7.1.4 Steepest Descent Method 383\u003c\/p\u003e \u003cp\u003e7.1.5 Newton Method 385\u003c\/p\u003e \u003cp\u003e7.1.6 Conjugate Gradient Method 387\u003c\/p\u003e \u003cp\u003e7.1.7 Simulated Annealing 389\u003c\/p\u003e \u003cp\u003e7.1.8 Genetic Algorithm 393\u003c\/p\u003e \u003cp\u003e7.2 Constrained Optimization 399\u003c\/p\u003e \u003cp\u003e7.2.1 Lagrange Multiplier Method 399\u003c\/p\u003e \u003cp\u003e7.2.2 Penalty Function Method 406\u003c\/p\u003e \u003cp\u003e7.3 MATLAB Built-In Functions for Optimization 409\u003c\/p\u003e \u003cp\u003e7.3.1 Unconstrained Optimization 409\u003c\/p\u003e \u003cp\u003e7.3.2 Constrained Optimization 413\u003c\/p\u003e \u003cp\u003e7.3.3 Linear Programming (LP) 416\u003c\/p\u003e \u003cp\u003e7.3.4 Mixed Integer Linear Programming (MILP) 423\u003c\/p\u003e \u003cp\u003e7.4 Neural Network[K-1] 433\u003c\/p\u003e \u003cp\u003e7.5 Adaptive Filter[Y-3] 439\u003c\/p\u003e \u003cp\u003e7.6 Recursive Least Square Estimation (RLSE)[Y-3] 443\u003c\/p\u003e \u003cp\u003eProblems 448\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Matrices and Eigenvalues 467\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Eigenvalues and Eigenvectors 468\u003c\/p\u003e \u003cp\u003e8.2 Similarity Transformation and Diagonalization 469\u003c\/p\u003e \u003cp\u003e8.3 Power Method 475\u003c\/p\u003e \u003cp\u003e8.3.1 Scaled Power Method 475\u003c\/p\u003e \u003cp\u003e8.3.2 Inverse Power Method 476\u003c\/p\u003e \u003cp\u003e8.3.3 Shifted Inverse Power Method 477\u003c\/p\u003e \u003cp\u003e8.4 Jacobi Method 478\u003c\/p\u003e \u003cp\u003e8.5 Gram-Schmidt Orthonormalization and \u003ci\u003eQR \u003c\/i\u003eDecomposition 481\u003c\/p\u003e \u003cp\u003e8.6 Physical Meaning of Eigenvalues\/Eigenvectors 485\u003c\/p\u003e \u003cp\u003e8.7 Differential Equations with Eigenvectors 489\u003c\/p\u003e \u003cp\u003e8.8 DoA Estimation with Eigenvectors[Y-3] 493\u003c\/p\u003e \u003cp\u003eProblems 499\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Partial Differential Equations 509\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Elliptic PDE 510\u003c\/p\u003e \u003cp\u003e9.2 Parabolic PDE 515\u003c\/p\u003e \u003cp\u003e9.2.1 The Explicit Forward Euler Method 515\u003c\/p\u003e \u003cp\u003e9.2.2 The Implicit Backward Euler Method 516\u003c\/p\u003e \u003cp\u003e9.2.3 The Crank-Nicholson Method 518\u003c\/p\u003e \u003cp\u003e9.2.4 Using the MATLAB function ‘pdepe()’ 520\u003c\/p\u003e \u003cp\u003e9.2.5 Two-Dimensional Parabolic PDEs 523\u003c\/p\u003e \u003cp\u003e9.3 Hyperbolic PDES 526\u003c\/p\u003e \u003cp\u003e9.3.1 The Explicit Central Difference Method 526\u003c\/p\u003e \u003cp\u003e9.3.2 Two-Dimensional Hyperbolic PDEs 529\u003c\/p\u003e \u003cp\u003e9.4 Finite Element Method (FEM) for Solving PDE 532\u003c\/p\u003e \u003cp\u003e9.5 GUI of MATLAB for Solving PDES – PDE tool 543\u003c\/p\u003e \u003cp\u003e9.5.1 Basic PDEs Solvable by PDEtool 543\u003c\/p\u003e \u003cp\u003e9.5.2 The Usage of PDEtool 545\u003c\/p\u003e \u003cp\u003e9.5.3 Examples of Using PDEtool to Solve PDEs 549\u003c\/p\u003e \u003cp\u003eProblems 559\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Mean Value Theorem 575\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Matrix Operations\/Properties 577\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Addition and Subtraction 578\u003c\/p\u003e \u003cp\u003eB.2 Multiplication 578\u003c\/p\u003e \u003cp\u003eB.3 Determinant 578\u003c\/p\u003e \u003cp\u003eB.4 Eigenvalues and Eigenvectors of a Matrix 579\u003c\/p\u003e \u003cp\u003eB.5 Inverse Matrix 580\u003c\/p\u003e \u003cp\u003eB.6 Symmetric\/Hermitian Matrix 580\u003c\/p\u003e \u003cp\u003eB.7 Orthogonal\/Unitary Matrix 581\u003c\/p\u003e \u003cp\u003eB.8 Permutation Matrix 581\u003c\/p\u003e \u003cp\u003eB.9 Rank 581\u003c\/p\u003e \u003cp\u003eB.10 Row Space and Null Space 581\u003c\/p\u003e \u003cp\u003eB.11 Row Echelon Form 582\u003c\/p\u003e \u003cp\u003eB.12 Positive Definiteness 582\u003c\/p\u003e \u003cp\u003eB.13 Scalar (Dot) Product and Vector (Cross) Product 583\u003c\/p\u003e \u003cp\u003eB.14 Matrix Inversion Lemma 584\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Differentiation W.R.T. A Vector 585\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D Laplace Transform 587\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix E Fourier Transform 589\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix F Useful Formulas 591\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix G Symbolic Computation 595\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eG.1 How to Declare Symbolic Variables and Handle Symbolic Expressions 595\u003c\/p\u003e \u003cp\u003eG.2 Calculus 597\u003c\/p\u003e \u003cp\u003eG.2.1 Symbolic Summation 597\u003c\/p\u003e \u003cp\u003eG.2.2 Limits 597\u003c\/p\u003e \u003cp\u003eG.2.3 Differentiation 598\u003c\/p\u003e \u003cp\u003eG.2.4 Integration 598\u003c\/p\u003e \u003cp\u003eG.2.5 Taylor Series Expansion 599\u003c\/p\u003e \u003cp\u003eG.3 Linear Algebra 600\u003c\/p\u003e \u003cp\u003eG.4 Solving Algebraic Equations 601\u003c\/p\u003e \u003cp\u003eG.5 Solving Differential Equations 601\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix H Sparse Matrices 603\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix I MATLAB 605\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 611\u003c\/p\u003e \u003cp\u003eIndex 613\u003c\/p\u003e \u003cp\u003eIndex for MATLAB Functions 619\u003c\/p\u003e \u003cp\u003eIndex for Tables 629\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eWon Y. Yang, PhD,\u003c\/b\u003e is a Professor in the Department of Electrical Engineering at Chung-Ang University in Seoul, Korea. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eWenwu Cao, PhD,\u003c\/b\u003e is a Professor in the Department of Materials Science and Engineering at Penn State University in University Park, Pennsylvania. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJaekwon Kim, PhD,\u003c\/b\u003e is a Professor in the Department of Electrical Engineering at Yongsei University in Wonju, Korea. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKyung W. Park, PhD,\u003c\/b\u003e is a Professor in the Department of Electrical Engineering at Yonsei University, Wonju, Korea. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eHo-Hyun Park, PhD,\u003c\/b\u003e is a Professor in the School of Electrical and Electronics Engineering at Chung-Ang University in Seoul, Korea. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJingon Joung, PhD,\u003c\/b\u003e is a Professor in the Department of Electrical Engineering at Chung-Ang University in Seoul, Korea. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJong-Suk Ro\u003c\/b\u003e is Creative Research Engineer Development at Brain Korea 21 Plus, Seoul National University in Seoul, Korea. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eHan L. Lee, PhD,\u003c\/b\u003e is a Professor in the Department of Electrical Engineering at Chung-Ang University in Seoul, Korea. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eCheol-Ho Hong\u003c\/b\u003e is Assistant Professor in the School of Electrical and Electronics Engineering at Chung-Ang University in Seoul, Korea. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eTaeho Im, PhD,\u003c\/b\u003e is a Professor in Oceanic IT Engineering at Hoseo University in Asan, Korea.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eThis new edition provides an updated approach for students, engineers, and researchers to apply numerical methods for solving problems using MATLAB\u003csup\u003e®\u003c\/sup\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThis accessible book makes use of MATLAB\u003csup\u003e®\u003c\/sup\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eApplied Numerical Methods Using MATLAB\u003csup\u003e®\u003c\/sup\u003e, Second Edition\u003c\/i\u003e 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eProvides examples and problems of solving electronic circuits and neural networks\u003c\/li\u003e \u003cli\u003eIncludes new sections on adaptive filters, recursive least-squares estimation, Bairstow's method for a polynomial equation, and more\u003c\/li\u003e \u003cli\u003eExplains Mixed Integer Linear Programing (MILP) and DOA (Direction of Arrival) estimation with eigenvectors\u003c\/li\u003e \u003cli\u003eAimed at students who do not like and\/or do not have time to derive and prove mathematical results\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eApplied Numerical Methods Using MATLAB\u003csup\u003e®\u003c\/sup\u003e, Second Edition\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988752842981,"sku":"NP9781119626800","price":131.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119626800.jpg?v=1761781453","url":"https:\/\/k12savings.com\/products\/applied-numerical-methods-using-matlab-isbn-9781119626800","provider":"K12savings","version":"1.0","type":"link"}