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Numerical Analysis - Timothy Sauer

Numerical Analysis

(Autor)

Buch | Hardcover
688 Seiten
2017 | 3rd edition
Pearson (Verlag)
978-0-13-469645-4 (ISBN)
CHF 328,55 inkl. MwSt
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For courses in Numerical Analysis.



 



Helps students construct and explore algorithms for solving science and engineering problems



Numerical Analysis, 3rd Edition is for students of engineering, science, mathematics, and computer science who have completed elementary calculus and matrix algebra. It covers both standard topics and some of the more advanced numerical methods used by computational scientists and engineers, while remaining readable and relevant for undergraduates. 




Sauer discusses the fundamental concepts of numerical analysis: convergence, complexity, conditioning, compression, and orthogonality. Throughout, Spotlight features comment on each of these concepts as they are addressed, and make connections to other expressions of the same principle elsewhere in the book. The popular Reality Check in each chapter gives concrete, relevant examples of the way numerical methods lead to solutions of important scientic and technological problems; they can be used to launch individual or team projects. MATLAB® is used throughout to demonstrate and implement numerical methods.

 

 



013469645X / 9780134696454  Numerical Analysis, 3/e

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.

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

Erscheinungsdatum
Verlagsort Upper Saddle River
Sprache englisch
Maße 203 x 257 mm
Gewicht 1280 g
Themenwelt Mathematik / Informatik Mathematik Analysis
ISBN-10 0-13-469645-X / 013469645X
ISBN-13 978-0-13-469645-4 / 9780134696454
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
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