Overview

The most readable and relevant numerical analysis text is now infused with web links at point-of-use. Numerical Analysis, 3rd Edition is written for students of engineering, science, mathematics, and computer science who have completed elementary calculus and matrix algebra. The book covers both standard topics and some of the more advanced numerical methods used by computational scientists and engineers, while maintaining a level appropriate for undergraduates. Students learn to construct and explore algorithms for solving science and engineering problems while situating these algorithms in a landscape of some potent and far-reaching principles. Specifically, the author cultivates a grasp of the fundamental concepts that permeate numerical analysis, including convergence, complexity, conditioning, compression, orthogonality, and its competing concerns of accuracy and efficiency. MATLAB® software is used both for exposition of algorithms and as a suggested platform for student assignments and projects. The 3rd Edition is web enhanced, with over 200 short URLs that take students beyond the book to useful digital resources created to support their use of the text.

Full Product Details

Author:   Timothy Sauer ,  Timothy Sauer
Publisher:   Pearson Education (US)
Imprint:   Pearson
Edition:   3rd edition
Dimensions:   Width: 20.30cm , Height: 2.80cm , Length: 25.70cm
Weight:   1.280kg
ISBN:  

9780134696454

ISBN 10:   013469645
Pages:   688
Publication Date:   21 December 2017
Audience:   College/higher education ,  Tertiary & Higher Education
Replaced By:   9780134696454
Format:   Hardback
Publisher's Status:   Active
Availability:   Available To Order  
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Table of Contents

CHAPTER 0      Fundamentals 0.1 Evaluating a Polynomial 0.2 Binary Numbers     0.2.1 Decimal to binary     0.2.2 Binary to decimal 0.3 Floating Point Representation of Real Numbers     0.3.1 Floating point formats     0.3.2 Machine representation     0.3.3 Addition of floating point numbers 0.4 Loss of Significance 0.5 Review of Calculus Software and Further Reading   CHAPTER 1      Solving Equations 1.1 The Bisection Method     1.1.1 Bracketing a root     1.1.2 How accurate and how fast? 1.2 Fixed-Point Iteration     1.2.1 Fixed points of a function     1.2.2 Geometry of Fixed-Point Iteration     1.2.3 Linear convergence of Fixed-Point Iteration     1.2.4 Stopping criteria 1.3 Limits of Accuracy     1.3.1 Forward and backward error     1.3.2 The Wilkinson polynomial     1.3.3 Sensitivity of root-finding 1.4 Newton’s Method     1.4.1 Quadratic convergence of Newton’s Method     1.4.2 Linear convergence of Newton’s Method 1.5 Root-Finding without Derivatives     1.5.1 Secant Method and variants     1.5.2 Brent’s Method Reality Check 1: Kinematics of the Stewart platform Software and Further Reading   CHAPTER 2      Systems of Equations 2.1 Gaussian Elimination     2.1.1 Naive Gaussian elimination     2.1.2 Operation counts 2.2 The LU Factorization     2.2.1 Matrix form of Gaussian elimination     2.2.2 Back substitution with the LU factorization     2.2.3 Complexity of the LU factorization 2.3 Sources of Error     2.3.1 Error magnification and condition number     2.3.2 Swamping 2.4 The PA = LU Factorization     2.4.1 Partial pivoting     2.4.2 Permutation matrices     2.4.3 PA = LU factorization Reality Check 2: The Euler–Bernoulli Beam 2.5 Iterative Methods     2.5.1 Jacobi Method     2.5.2 Gauss–Seidel Method and SOR     2.5.3 Convergence of iterative methods     2.5.4 Sparse matrix computations 2.6 Methods for symmetric positive-definite matrices     2.6.1 Symmetric positive-definite matrices     2.6.2 Cholesky factorization     2.6.3 Conjugate Gradient Method     2.6.4 Preconditioning 2.7 Nonlinear Systems of Equations     2.7.1 Multivariate Newton’s Method     2.7.2 Broyden’s Method Software and Further Reading   CHAPTER 3      Interpolation 3.1 Data and Interpolating Functions     3.1.1 Lagrange interpolation     3.1.2 Newton’s divided differences     3.1.3 How many degree d polynomials pass through n points?     3.1.4 Code for interpolation     3.1.5 Representing functions by approximating polynomials 3.2 Interpolation Error     3.2.1 Interpolation error formula     3.2.2 Proof of Newton form and error formula     3.2.3 Runge phenomenon 3.3 Chebyshev Interpolation     3.3.1 Chebyshev’s theorem     3.3.2 Chebyshev polynomials     3.3.3 Change of interval 3.4 Cubic Splines     3.4.1 Properties of splines     3.4.2 Endpoint conditions 3.5 Bézier Curves Reality Check 3: Fonts from Bézier curves Software and Further Reading CHAPTER 4      Least Squares 4.1 Least Squares and the Normal Equations     4.1.1 Inconsistent systems of equations     4.1.2 Fitting models to data     4.1.3 Conditioning of least squares 4.2 A Survey of Models     4.2.1 Periodic data     4.2.2 Data linearization 4.3 QR Factorization     4.3.1 Gram–Schmidt orthogonalization and least squares     4.3.2 Modified Gram–Schmidt orthogonalization     4.3.3 Householder reflectors 4.4 Generalized Minimum Residual (GMRES) Method     4.4.1 Krylov methods     4.4.2 Preconditioned GMRES 4.5 Nonlinear Least Squares     4.5.1 Gauss–Newton Method     4.5.2 Models with nonlinear parameters     4.5.3 The Levenberg–Marquardt Method Reality Check 4: GPS, Conditioning, and Nonlinear Least Squares Software and Further Reading CHAPTER 5      Numerical Differentiation and Integration 5.1 Numerical Differentiation     5.1.1 Finite difference formulas     5.1.2 Rounding error     5.1.3 Extrapolation     5.1.4 Symbolic differentiation and integration 5.2 Newton–Cotes Formulas for Numerical Integration     5.2.1 Trapezoid Rule     5.2.2 Simpson’s Rule     5.2.3 Composite Newton–Cotes formulas     5.2.4 Open Newton–Cotes Methods 5.3 Romberg Integration 5.4 Adaptive Quadrature 5.5 Gaussian Quadrature Reality Check 5: Motion Control in Computer-Aided Modeling Software and Further Reading CHAPTER 6      Ordinary Differential Equations 6.1 Initial Value Problems     6.1.1 Euler’s Method     6.1.2 Existence, uniqueness, and continuity for solutions     6.1.3 First-order linear equations 6.2 Analysis of IVP Solvers     6.2.1 Local and global truncation error     6.2.2 The explicit Trapezoid Method     6.2.3 Taylor Methods 6.3 Systems of Ordinary Differential Equations     6.3.1 Higher order equations     6.3.2 Computer simulation: the pendulum     6.3.3 Computer simulation: orbital mechanics 6.4 Runge–Kutta Methods and Applications     6.4.1 The Runge–Kutta family     6.4.2 Computer simulation: the Hodgkin–Huxley neuron     6.4.3 Computer simulation: the Lorenz equations Reality Check 6: The Tacoma Narrows Bridge 6.5 Variable Step-Size Methods     6.5.1 Embedded Runge–Kutta pairs     6.5.2 Order 4/5 methods 6.6 Implicit Methods and Stiff Equations 6.7 Multistep Methods     6.7.1 Generating multistep methods     6.7.2 Explicit multistep methods     6.7.3 Implicit multistep methods Software and Further Reading   CHAPTER 7      Boundary Value Problems 7.1 Shooting Method     7.1.1 Solutions of boundary value problems     7.1.2 Shooting Method implementation Reality Check 7: Buckling of a Circular Ring 7.2 Finite Difference Methods     7.2.1 Linear boundary value problems     7.2.2 Nonlinear boundary value problems 7.3 Collocation and the Finite Element Method     7.3.1 Collocation     7.3.2 Finite elements and the Galerkin Method Software and Further Reading   CHAPTER 8      Partial Differential Equations 8.1 Parabolic Equations     8.1.1 Forward Difference Method     8.1.2 Stability analysis of Forward Difference Method     8.1.3 Backward Difference Method     8.1.4 Crank–Nicolson Method 8.2 Hyperbolic Equations     8.2.1 The wave equation     8.2.2 The CFL condition 8.3 Elliptic Equations     8.3.1 Finite Difference Method for elliptic equations Reality Check 8: Heat distribution on a cooling fin     8.3.2 Finite Element Method for elliptic equations 8.4 Nonlinear partial differential equations     8.4.1 Implicit Newton solver     8.4.2 Nonlinear equations in two space dimensions Software and Further Reading   CHAPTER 9      Random Numbers and Applications 9.1 Random Numbers     9.1.1 Pseudo-random numbers     9.1.2 Exponential and normal random numbers 9.2 Monte Carlo Simulation     9.2.1 Power laws for Monte Carlo estimation     9.2.2 Quasi-random numbers 9.3 Discrete and Continuous Brownian Motion     9.3.1 Random walks     9.3.2 Continuous Brownian motion 9.4 Stochastic Differential Equations     9.4.1 Adding noise to differential equations     9.4.2 Numerical methods for SDEs Reality Check 9: The Black–Scholes Formula Software and Further Reading CHAPTER 10      Trigonometric Interpolation and the FFT 10.1 The Fourier Transform     10.1.1 Complex arithmetic     10.1.2 Discrete Fourier Transform     10.1.3 The Fast Fourier Transform 10.2 Trigonometric Interpolation     10.2.1 The DFT Interpolation Theorem     10.2.2 Efficient evaluation of trigonometric functions 10.3 The FFT and Signal Processing     10.3.1 Orthogonality and interpolation     10.3.2 Least squares fitting with trigonometric functions     10.3.3 Sound, noise, and filtering Reality Check 10: The Wiener Filter Software and Further Reading   CHAPTER 11      Compression 11.1 The Discrete Cosine Transform     11.1.1 One-dimensional DCT     11.1.2 The DCT and least squares approximation 11.2 Two-Dimensional DCT and Image Compression     11.2.1 Two-dimensional DCT     11.2.2 Image compression     11.2.3 Quantization 11.3 Huffman Coding     11.3.1 Information theory and coding     11.3.2 Huffman coding for the JPEG format 11.4 Modified DCT and Audio Compression     11.4.1 Modified Discrete Cosine Transform     11.4.2 Bit quantization Reality Check 11: A Simple Audio Codec Software and Further Reading CHAPTER 12      Eigenvalues and Singular Values 12.1 Power Iteration Methods     12.1.1 Power Iteration     12.1.2 Convergence of Power Iteration     12.1.3 Inverse Power Iteration     12.1.4 Rayleigh Quotient Iteration 12.2 QR Algorithm     12.2.1 Simultaneous iteration     12.2.2 Real Schur form and the QR algorithm     12.2.3 Upper Hessenberg form Reality Check 12: How Search Engines Rate Page Quality 12.3 Singular Value Decomposition     12.3.1 Finding the SVD in general     12.3.2 Special case: symmetric matrices 12.4 Applications of the SVD     12.4.1 Properties of the SVD     12.4.2 Dimension reduction     12.4.3 Compression     12.4.4 Calculating the SVD Software and Further Reading   CHAPTER 13      Optimization 13.1 Unconstrained Optimization without Derivatives     13.1.1 Golden Section Search     13.1.2 Successive parabolic interpolation     13.1.3 Nelder–Mead search 13.2 Unconstrained Optimization with Derivatives     13.2.1 Newton’s Method     13.2.2 Steepest Descent     13.2.3 Conjugate Gradient Search Reality Check 13: Molecular Conformation and Numerical Optimization Software and Further Reading Appendix A A.1 Matrix Fundamentals A.2 Systems of linear equations A.3 Block Multiplication A.4 Eigenvalues and Eigenvectors A.5 Symmetric Matrices A.6 Vector Calculus   Appendix B B.1 Starting MATLAB B.2 Graphics B.3 Programming in MATLAB B.4 Flow Control B.5 Functions B.6 Matrix Operations B.7 Animation and Movies   ANSWERS TO SELECTED EXERCISES BIBLIOGRAPHY INDEX

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Timothy Sauer earned his Ph.D. in mathematics at the University of California–Berkeley in 1982, and is currently a professor at George Mason University. He has published articles on a wide range of topics in applied mathematics, including dynamical systems, computational mathematics, and mathematical biology.

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