Numerical Analysis

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A01_SAUE6454_03_SE_FM PEARSON/SAUER August 8 2017 16:31. Page-i. Numerical Analysis. T H I R D E D I T I O N. Timothy Sauer. George Mason University

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Numerical Analysis. S E C O N D E D I T I O N. Timothy Sauer. George Mason University. Boston Columbus Indianapolis New York San Francisco Upper Saddle

Numerical Analysis (Second Edition)

The latter are envisaged to cover such topics as numerical linear algebra the numerical solution of ordinary and partial differential equations

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Numerical Analysis. S E C O N D E D I T I O N. Timothy Sauer. George Mason University. Boston Columbus Indianapolis New York San Francisco Upper Saddle

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Names: Sauer Tim author Title: Numerical analysis / Timothy Sauer George Mason University Description: Third edition Hoboken : Pearson [2019] Includes bibliographical references and index Identi?ers: LCCN 2017028491 ISBN 9780134696454 (alk paper) ISBN 013469645X (alk paper) Subjects: LCSH: Numerical analysis Mathematical

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Dedication To the memory of Ed Conway1 who along with his colleagues at Tulane University provided a stable adaptive and inspirational starting point for my career 1Edward Daire Conway III (1937{1985) was a student of Eberhard Friedrich Ferdinand Hopf at the

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Tim Sauer George Mason University Introduction Mathematical approximations have been used since ancient times to estimate solutions but with the rise of digital computing the field of numerical analysis has become a discipline in its own right Numerical analysts develop and study algorithms that provide

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numerical analysis has enabled the development of pocket calculators and computer software to make this routine But numerical analysis has done much more than this We will see that far more complex functions de?ned e g only implicitly can be evaluated just as easily and with the same tech-nology

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31 mar 2023 · Anthony Yeates tim sauer Numerical Integration; Numerical Methods Chapter 11 Iteration Method to solve Algebraic Equations NUMERICAL ANALYSIS

Is numerical analysis a mathematical subject?

As a result, numerical analysis is frequently presented as an elementary subject. As a corollary, most students miss exposure to numerical analysis as a mathemat- ical subject. We hope to provide an alternative. Several books written some decades ago addressed speci?cally a mathe- matical audience, e.g., [80, 84, 86].

Should numerical analysis and computational analysis be intertwined?

Ideally, both should be intertwined, as numerical analysis could well be called computa- tional analysis because it is the analysis of computational algorithms involv- ing realnumbers. We present many computational algorithmsand encourage computational exploration.

Are there any good books on numerical analysis?

Today there are many good books on numerical analysis at the graduate level, including general texts [47, 134] as well as more specialized texts. We reference many of the latter at the ends of chapters where we suggest fur- ther reading in particular areas.

Why is numerical analysis important?

“...by faith and faith alone, embrace, believing where we cannot prove,” from In Memoriam by Alfred Lord Ten- nyson, a memorial to Arthur Hallum. Numerical analysis provides the foundations for a major paradigm shift in what we understand as an acceptable “answer” to a scienti?c or techni- cal question.

A01_SAUE6454_03_SE_FM PEARSON/SAUER August 8, 2017 16:31 Page-i

THIRD EDITION

Timothy SauerGeorge Mason University

A01_SAUE6454_03_SE_FM PEARSON/SAUER August 8, 2017 16:31 Page-ii

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Title: Numerical analysis / Timothy Sauer, George Mason University. Description: Third edition. | Hoboken : Pearson, [2019] | Includes bibliographical references and index. Identifiers: LCCN 2017028491| ISBN 9780134696454 (alk. paper) |

ISBN 013469645X (alk. paper)

Subjects: LCSH: Numerical analysis. | Mathematical analysis. Classification: LCC QA297 .S348 2019 | DDC 518-dc23 LC record available at https://lccn.loc.gov/2017028491 117

ISBN 10: 0-13-469645-X

ISBN 13: 978-0-13-469645-4

A01_SAUE6454_03_SE_FM PEARSON/SAUER August 8, 2017 16:31 Page-iii

PREFACE xi

CHAPTER 0 Fundamentals 1

0.1Evaluating a Polynomial 1

0.2Binary Numbers 5

0.2.1 Decimal to binary 6

0.2.2 Binary to decimal 7

0.3Floating Point Representation of Real Numbers 8

0.3.1 Floating point formats 8

0.3.2 Machine representation 12

0.3.3 Addition of floating point numbers 14

0.4Loss of Significance 17

0.5Review of Calculus 21

Software and Further Reading 24

CHAPTER 1 Solving Equations 26

1.1The Bisection Method 27

1.1.1 Bracketing a root 27

1.1.2 How accurate and how fast? 30

1.2Fixed-Point Iteration 33

1.2.1 Fixed points of a function 33

1.2.2 Geometry of Fixed-Point Iteration 36

1.2.3 Linear convergence of Fixed-Point Iteration 36

1.2.4 Stopping criteria 42

1.3Limits of Accuracy 46

1.3.1 Forward and backward error 46

1.3.2 The Wilkinson polynomial 49

1.3.3 Sensitivity of root-finding 50

1.4Newton's Method 54

1.4.1 Quadratic convergence of Newton's Method 56

1.4.2 Linear convergence of Newton's Method 58

1.5Root-Finding without Derivatives 64

1.5.1 Secant Method and variants 64

1.5.2 Brent's Method 67

Reality Check 1:Kinematics of the Stewart platform 70

Software and Further Reading 72

A01_SAUE6454_03_SE_FM PEARSON/SAUER August 8, 2017 16:31 Page-iv iv |Contents

CHAPTER 2 Systems of Equations 74

2.1Gaussian Elimination 74

2.1.1 Naive Gaussian elimination 75

2.1.2 Operation counts 77

2.2The LU Factorization 82

2.2.1 Matrix form of Gaussian elimination 82

2.2.2 Back substitution with the LU factorization 85

2.2.3 Complexity of the LU factorization 86

2.3Sources of Error 89

2.3.1 Error magnification and condition number 89

2.3.2 Swamping 95

2.4The PA=LU Factorization 99

2.4.1 Partial pivoting 99

2.4.2 Permutation matrices 101

2.4.3 PA=LU factorization 102

Reality Check 2:The Euler-Bernoulli Beam 107

2.5Iterative Methods 110

2.5.1 Jacobi Method 111

2.5.2 Gauss-Seidel Method and SOR 113

2.5.3 Convergence of iterative methods 116

2.5.4 Sparse matrix computations 117

2.6Methods for symmetric positive-definite matrices 122

2.6.1 Symmetric positive-definite matrices 122

2.6.2 Cholesky factorization 124

2.6.3 Conjugate Gradient Method 127

2.6.4 Preconditioning 132

2.7Nonlinear Systems of Equations 136

2.7.1 Multivariate Newton's Method 136

2.7.2 Broyden's Method 139

Software and Further Reading 143

CHAPTER 3 Interpolation 144

3.1Data and Interpolating Functions 145

3.1.1 Lagrange interpolation 146

3.1.2 Newton's divided differences 147

3.1.3 How many degreedpolynomials pass throughn

points? 150

3.1.4 Code for interpolation 151

3.1.5 Representing functions by approximating polynomials 153

3.2Interpolation Error 157

3.2.1 Interpolation error formula 158

3.2.2 Proof of Newton form and error formula 159

3.2.3 Runge phenomenon 162

3.3Chebyshev Interpolation 164

3.3.1 Chebyshev's theorem 165

3.3.2 Chebyshev polynomials 167

3.3.3 Change of interval 169

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Contents|v

3.4Cubic Splines 173

3.4.1 Properties of splines 174

3.4.2 Endpoint conditions 180

3.5Bézier Curves 185

Reality Check 3:Fonts from Bézier curves 190

Software and Further Reading 194

CHAPTER 4 Least Squares 196

4.1Least Squares and the Normal Equations 196

4.1.1 Inconsistent systems of equations 197

4.1.2 Fitting models to data 201

4.1.3 Conditioning of least squares 205

4.2A Survey of Models 208

4.2.1 Periodic data 208

4.2.2 Data linearization 211

4.3QR Factorization 220

4.3.1 Gram-Schmidt orthogonalization and least squares 220

4.3.2 Modified Gram-Schmidt orthogonalization 227

4.3.3 Householder reflectors 228

4.4Generalized Minimum Residual (GMRES) Method 235

4.4.1 Krylov methods 235

4.4.2 Preconditioned GMRES 237

4.5Nonlinear Least Squares 240

4.5.1 Gauss-Newton Method 240

4.5.2 Models with nonlinear parameters 243

4.5.3 The Levenberg-Marquardt Method. 245

Reality Check 4:GPS, Conditioning, and Nonlinear Least Squares 248

Software and Further Reading 251

CHAPTER 5 Numerical Differentiation andIntegration 253

5.1Numerical Differentiation 254

5.1.1 Finite difference formulas 254

5.1.2 Rounding error 257

5.1.3 Extrapolation 259

5.1.4 Symbolic differentiation and integration 261

5.2Newton-Cotes Formulas for Numerical Integration 264

5.2.1 Trapezoid Rule 265

5.2.2 Simpson's Rule 267

5.2.3 Composite Newton-Cotes formulas 269

5.2.4 Open Newton-Cotes Methods 272

5.3Romberg Integration 276

5.4Adaptive Quadrature 279

5.5Gaussian Quadrature 284

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