Fundamentals of Numerical Mathematics for Physicists and Engineers

Fundamentals of Numerical Mathematics for Physicists and Engineers
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Introduces the fundamentals of numerical mathematics and illustrates its applications to a wide variety of disciplines in physics and engineering   Applying numerical mathematics to solve scientific problems, this book helps readers understand the mathematical and algorithmic elements that lie beneath numerical and computational methodologies in order to determine the suitability of certain techniques for solving a given problem. It also contains examples related to problems arising in classical mechanics, thermodynamics, electricity, and quantum physics. Fundamentals of Numerical Mathematics for Physicists and Engineers is presented in two parts. Part I addresses the root finding of univariate transcendental equations, polynomial interpolation, numerical differentiation, and numerical integration. Part II examines slightly more advanced topics such as introductory numerical linear algebra, parameter dependent systems of nonlinear equations, numerical Fourier analysis, and ordinary differential equations (initial value problems and univariate boundary value problems). Chapters cover: Newton’s method, Lebesgue constants, conditioning, barycentric interpolatory formula, Clenshaw-Curtis quadrature, GMRES matrix-free Krylov linear solvers, homotopy (numerical continuation), differentiation matrices for boundary value problems, Runge-Kutta and linear multistep formulas for initial value problems. Each section concludes with Matlab hands-on computer practicals and problem and exercise sets. This book:  Provides a modern perspective of numerical mathematics by introducing top-notch techniques currently used by numerical analysts Contains two parts, each of which has been designed as a one-semester course Includes computational practicals in Matlab (with solutions) at the end of each section for the instructor to monitor the student's progress through potential exams or short projects Contains problem and exercise sets (also with solutions) at the end of each section  Fundamentals of Numerical Mathematics for Physicists and Engineers is an excellent book for advanced undergraduate or graduate students in physics, mathematics, or engineering. It will also benefit students in other scientific fields in which numerical methods may be required such as chemistry or biology.

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Alvaro Meseguer. Fundamentals of Numerical Mathematics for Physicists and Engineers

Table of Contents

List of Tables

List of Illustrations

Guide

Pages

Fundamentals of Numerical Mathematics for Physicists and Engineers

About the Author

Preface

Acknowledgments

1 Solution Methods for Scalar Nonlinear Equations. 1.1 Nonlinear Equations in Physics

1.2 Approximate Roots: Tolerance

1.2.1 The Bisection Method

1.3 Newton's Method

1.4 Order of a Root‐Finding Method

1.5 Chord and Secant Methods

Practical 1.1 Sliding Particles over Surfaces

1.6 Conditioning

1.7 Local and Global Convergence

Complementary Reading

Practical 1.2 Throwing Balls and Conditioning

Problems and Exercises

Notes

2 Polynomial Interpolation. 2.1 Function Approximation

2.2 Polynomial Interpolation

2.3 Lagrange's Interpolation

2.3.1 Equispaced Grids

Practical 2.1 Lagrange Equispaced Interpolation

2.4 Barycentric Interpolation

Interpolation Forms (Summary)

2.5 Convergence of the Interpolation Method

Result I Weiertrass' Approximation Theorem

Result II Cauchy's Remainder for Interpolation

2.5.1 Runge's Counterexample

2.6 Conditioning of an Interpolation

Practical 2.2 Conditioning of Equispaced Nodes

2.7 Chebyshev's Interpolation

Convergence Rate of Chebyshev's Interpolation

Practical 2.3 Barycentric‐Chebyshev Interpolation

Complementary Reading

Problems and Exercises

Notes

3 Numerical Differentiation

3.1 Introduction

Numerical Differentiation

3.2 Differentiation Matrices

Summary: Differentiation Matrix

3.3 Local Equispaced Differentiation

3.4 Accuracy of Finite Differences

Nodal Error of Interpolatory Differentiation

Practical 3.1 Equispaced (Local) Differentiation

3.5 Chebyshev Differentiation

Reducing (Constant ): Local Differentiation

Reducing the Error by Increasing : Global Differentiation

Chebyshev Differentiation Matrix

Practical 3.2 Local Versus Global Numerical Differentiation

Complementary Reading

Problems and Exercises

Notes

4 Numerical Integration. 4.1 Introduction

4.2 Interpolatory Quadratures

Summary: Interpolatory Quadrature Formulas

4.2.1 Newton–Cotes Quadratures

Closed and Open Newton–Cotes Formulas

4.2.2 Composite Quadrature Rules

4.3 Accuracy of Quadrature Formulas

Error and Degree of Exactness of a Quadrature Formula

Quadrature Errors of Newton–Cotes Formulas

Quadrature Errors of Composite Newton–Cotes Formulas

Practical 4.1 Local Versus Global Equispaced Quadratures

4.4 Clenshaw–Curtis Quadrature

Chebyshev Polynomials

Summary: Clenshaw–Curtis Quadrature

Practical 4.2 Gravitational Potential of a Ring

4.5 Integration of Periodic Functions

Geometrical Convergence of Composite Trapezoidal Rule

4.6 Improper Integrals

4.6.1 Improper Integrals of the First Kind

4.6.2 Improper Integrals of the Second Kind

Practical 4.3 Beads Moving Along Wires

Complementary Reading

Problems and Exercises

Notes

5 Numerical Linear Algebra. 5.1 Introduction

5.2 Direct Linear Solvers

5.2.1 Diagonal and Triangular Systems

5.2.2 The Gaussian Elimination Method

Summary: Gaussian Elimination Method (GEM)

5.3 LU Factorization of a Matrix

Property 1: Product of Lower Triangular Matrices

Property 2: Inverse of a Non‐singular Lower Triangular Matrix

Definition (Dyadic Product of Two Vectors)

Property (Existence and Uniqueness of LU‐Factorization)

5.3.1 Solving Systems with LU

5.3.2 Accuracy of LU

5.4 LU with Partial Pivoting

Definition: Permutation Matrix

Definition: Exchange Matrix

Property: Action of an Exchange Matrix

Practical 5.1 Equivalent Resistance

5.5 The Least Squares Problem

5.5.1 QR Factorization

Orthogonal Matrix

Householder Transformation (Reflector)

Property

Summary (Vector Reduction Using Reflectors)

Theorem (QR‐Factorization)

5.5.2 Linear Data Fitting

Practical 5.2 Least Squares Polynomial Fit

5.6 Matrix Norms and Conditioning

Matrix Norm

Induced Vector or Operator Norm of a Matrix

Condition Number of a Matrix

Accuracy of GEM-LU Solutions with Partial Pivoting

5.7 Gram-Schmidt Orthonormalization

5.7.1 Instability of CGS: Reorthogonalization

Practical 5.3 Gram–Schmidt and Reorthogonalization

5.8 Matrix‐Free Krylov Solvers

Krylov24 Spaces and Krylov Matrices

Practical 5.4 Matrix‐Free Krylov Solvers (GMRES)

Complementary Reading

Problems and Exercises

Notes

6 Systems of Nonlinear Equations

6.1 Newton's Method for Nonlinear Systems

Newton's Iteration for Nonlinear Systems

Quadratic Convergence (Newton–Kantorovich Conditions)

Practical 6.1 Newton's Method in

6.2 Nonlinear Systems with Parameters

6.3 Numerical Continuation (Homotopy)

Practical 6.2 Numerical Continuation

Complementary Reading

Problems and Exercises

Notes

7 Numerical Fourier Analysis

7.1 The Discrete Fourier Transform

Summary: DFT and IDFT

7.1.1 Time–Frequency Windows

Summary: Frequency Window

7.1.2 Aliasing

Practical 7.1 Discrete Fourier Transform (DFT)

7.2 Fourier Differentiation

Summary: Fourier Differentiation

Complementary Reading

Problems and Exercises

Notes

8 Ordinary Differential Equations

8.1 Boundary Value Problems

8.1.1 Bounded Domains

BVP (Bounded Domain)

Practical 8.1 The Suspended Chain

8.1.2 Periodic Domains

8.1.3 Unbounded Domains

8.2 The Initial Value Problem

8.2.1 Runge–Kutta One‐Step Formulas

8.2.2 Linear Multistep Formulas

Linear Multistep Formulas (LMSF)

Practical 8.2 Gravitational Motion

8.2.3 Convergence of Time‐Steppers

Order of Convergence of One‐Step Methods

Order of Convergence of an ‐step LMSF

Order of Convergence of Explicit Runge–Kutta Methods

Order of Convergence of Explicit AB and Implicit BDF Formulas

8.2.4 A‐Stability

Absolute Stability of a Time‐Stepper

Characteristic Polynomials Associated with an LMSF

Homogeneous Linear Difference Equations

A‐Stability

8.2.5 A‐Stability in Nonlinear Systems: Stiffness

Absolute Stability of a Time‐Stepper: Linear Systems

Linearized Criterion for Stability

Stiffness

Practical 8.3 Shock Waves (Method of Lines)

Complementary Reading

Problems and Exercises

Notes

1 Solutions to Problems and Exercises. Chapter 1

Practical 1.1

Practical 1.2

Chapter 2

Practical 2.3

Chapter 3

Practical 3.1

Practical 3.2

Chapter 4

Practical 4.1

Practical 4.2

Practical 4.3

Chapter 5

Practical 5.1

Practical 5.2

Practical 5.3

Practical 5.4

Chapter 6

Practical 6.1

Practical 6.2

Chapter 7

Practical 7.1

Chapter 8

Practical 8.1

Practical 8.2

Practical 8.3

Glossary of Mathematical Symbols

Bibliography

Index

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Alvaro Meseguer

Department of PhysicsUniversitat Polit`ecnica de Catalunya – UPC BarcelonaTech

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Expression (1.13) is less formal but certainly provides more insight. For example, in the linear case (1.13) implies that , since for . In other words, the sequence must be monotonically decreasing in order to have linear convergence. According to Table 1.1, , and therefore the bisection method does not qualify to have linear convergence in the sense of (1.11)10

We can numerically estimate the actual order of convergence by taking the logarithm of both sides of (1.13) and setting the quantities , , and , so that the expression now reads

.....

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