C++数值算法（第二版英文版）|国外计算机科学教材系列|（美）William H.Press Saul A.Teukolsky William T.Vetterlin|电子工业出版社

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C++数值算法（第二版英文版）

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原书名：Numerical Recipes in C++： The Art of Scientific Computing，Second Edition	原出版社：Cambridge University Press
作者：（美）William H.Press Saul A.Teukolsky William T.Vetterlin	出版社：电子工业出版社
译者：	丛书名：国外计算机科学教材系列
出版日期：2003-4-1	上架日期：2005-10-8
ISBN：750538631X	页数：版次：1-1
开本：16开	装帧：

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| 大封面 | 封底 | 前言 | 内容简介 | 序言 | 目录 | 作者简介 | 译者简介 | 作者序 | 译者序 |

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内容简介
本书选材内容丰富，包括了当代科学计算过程中涉及的大量内容：求特殊函数值、随机数、排序、最优化、快速傅里叶变换、谱分析、小波变换、统计描述和数据建模、偏微分方程数值解、若干编码算法和任意精度计算等。本书科学性和实用性统一，不仅对每种算法进行了数学分析和比较，而且根据作者经验对算法给出了评论和建议，并在此基础上提供了用C++语言编写的实用程序，本书中的345个程序构成了C++语言的数值计算程序库。
本书可以作为从事科学计算的科技工作者的工具书，计算机软件开发者的参考书，也可以作为大学本科生和研究生的参考书或教材。

目录
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1 Preliminaries
     1.0 Introduction
     1.1 Program Organization and Control Structures
     1.2 Some C++ Conventions for Scientific Computing
     1.3 Imptementation of the Vector and Matrix Classes
     1.4 Error, Accuracy, and Stability
     2 Solution of Linear Algebraic Equations
     2.0 Introduction
     2.1 Gauss-Jordan Elimination
     2.2 Gaussian Elimination with Backsubstitution
     2.3 LU Decomposition and Its Applications 
     2.4 Tridiagonal and Band Diagonal Systems of Equations
     2.5 Iterative Improvement of a Solution to Linear Equations
     2.6 Singular Value Decomposition
     2.7 Sparse Linear Systems
     2.8 Vandermonde Matrices and Toeplitz Matrices
     2.9 Cholesky Decomposition
     2.10 QR Decomposition
     2.11 Is Matrix Inversion an N3 Process?
     3 Interpolation and Extrapolation
     3.0 Introduction
     3.1 Polyaomial Interpolation and Extrapolation
     3.2 Rational Function Interpolation and Extrapolation
     3.3 Cubic Spline Interpolation
     3.4 How to Search an Ordered Table
     3.5 Coefficients of the Interpolating Polynomial
     3.6 Interpolation in Two or More Dimensions
     4 Integration of Functions
     4.O Introduction
     4.1 Classical Formulas for Equally Spaced Abscissas
     4.2 Elementary Algorithms
     4.3 Romberg Integration
     4.4 Improper Integrals
     4.5 Gaussian Quadratures and Orthogonal Polynomials
     4.6 Multidimensional Integrals
     5 Evaluation of Functions
     5.0 Introduction
     5.1 Series and Their Convergence
     5.2 Evaluation of Continued Fractions
     5.3 Polynomials and Rational Functions
     5.4 Complex Arithmetic
     5.5 Recurrence Relations and Clenshaw's Recurrence Formula
     5.6 Quadratic and Cubic Equations 
     5.7 Numerical Derivatives
     5.8 Chebyshev Approximation 
     5.9 Derivatives or Integrals of a Chebyshev-approximated Function
     3.10 Polynomial Approximation from Chebyshev Coefficients
     5.11 Economization of Power Series
     5.12 Pade Approximants
     5.13 Rational Chebyshev Appmximation
     5.14 Evaluation of Functions by Path Integration
     6 Speclal Functions
     6.0 Introduction
     6.1 Gamma Function, Beta Function, Factorials. Binomial Coefficients
     6.2 Incomplete Gamma Function, Error Function. Chi-Square  Probability Function, Cumulative Poisson Function
     6.3 Exponential Integrals
     6.4 Incomplete Beta Function, Student's Distribution, F-Distribution Cumulative Binomial Distribution 
     6.5 Bessel Functions of Integer Order
     6.6 Modified Bessel Functions of Integer Order
     6.7 Bessel Functions of Fractional Order, Airy Functions, Spherical Bessel Functions
     6.8 Spherical Harmonics
     6.9 Fresnel Integrals, Cosine and Sine Integrals
     6.10 Dawson's Inegral 
     6.ll Elliptic Integrals and Jacobian Elliptic Functions
     6.12 Hypergeometric Functions 
7 Random Numbers
     7.O Introduction
     7.1 Uniform Deviates
     7.2 Transformation Method: Exponential and Normal Deviates
     7.3 Rejection Method: Gamma. Poisson, Binomial Deviates
     7.4 Generation of Random Bits
     7.5 Random Sequences Based on Data Encryption
     7.6 Simple Monte Carlo Integration
     7.7 Quasi- (that is, Sub-) Random Sequences
     7.8 Adaptive and Recursive Monte Carlo Methods
     8 Sorting
     8.0 Introduction
     8.1 Straight Insertion and Shell's Method
     8.2 Quicksort
     8.3 Heapsort
     8.4 Indexing and Ranking
     8.5 Selecting the Mth Largest
     8.6 Determination of Equivalence Classes
     9 Root Finding and Nonlinear Sets of Equations
     9.0 Introduction
     9.l Bracketing and Bisection
     9.2 Secant Method, False Position Method, and Ridders' Method
     9.3 Van Wijngaarden-Dekker-Brent Method
     9.4 Newton-Raphson Metkod Using Derivative
     9.5 Roots of Polynomials
     9.6 Newton-Raphson Mealod for Nonlinear Systems of Equations
     9.7 Globally Convergent Methods for Nonlinear Systems of Equations
     10 Minimization or Maximization of Functions
     10.0 Introduction
     10.1 Golden Section Search in One Dimension
     10.2 Parabolic lnterpolation and Brent's Method in One Dimension
     10.3 One-Dimensional Search with First Derivatives
     10.4 Downhill Simplex Method in Multidimensions
     10.5 Direction Set (Powell's) Methods in Multidimensions
     10.6 Conjugate Gadient Methods in Multidimensions
     10.7 Variable Metric Methods in Multidimensions
     10.8 Linear Pro
     10.9 Simulated Annealing Meathods
     11 Eigensystems
     11.0 Introduction
     11.1 Jacobi Transformations of a Symmetric Matrix
     11.2 Reduction of a Symmetric Matrix to Tridiagonal Form
     Givens and Householder Reductions
     11.3 Eigenvalues and Eigenvectors of a Tridiagonal Matrix
     11.4 Hermitian Matrices
     11.5 Reduction of a General Matrix to Hessenberg Form
     11.6 The QR Algorithm for Real Hessenbery Matrices
     11.7 Improving Eigenvalues and/or Finding Eigenvectors by
     Inverse Iteration
     12 Fast Fourier Transform
     12.0 Introduction
     12.1 Fourier Transform of Discretely Sampled Data
     12.2 Fast Fourier Transform (FFT)
     12.3 FFT of Real Functions, Sine and Cosine Transforms
     12.4 FFT in Two or More Dimensions
     12.5 Fourier Transforms of Real Data in Two and Three Dimensions
     12.6 External Storage or Memory-Local FFTs
     13 Fourier and Spectral Applications
     13.0 Introduction
     13.1 Convolution and Deconvolution Using the FFT
     13.2 Correlation and Autocorrelation Using the FFT
     13.3 Optimal (Wiener) Filterin with the FFT
     13.4 Power Spectrum Estimation Using the FFT
     13.5 Digital Filtering in ahe Time Domain
     13.6 Linear Prediction and Linear Predictive Coding
     13.7 Power Spectrum Estimation by the Maximum Entropy
     (All Poles) Method
     13.8 Spectral Analysis of Unevenly Sampled Data
     13.9 Computing Fourier Integrals Using Ule FFT
     13.10 Wavelet Transforms
     13.11 Numerical Use of the Sampling Theorem
     14 Statistical Deseription of Data
     14.O Introduction
     14.1 Moments of a Distribution: Mean, Variance, Skewness,
     and So Forth
     14.2 Do Two Distributions Have the Same Means or Variances?
     14.3 Are Two Distributions Different?
     14.4 Contingency Table Analysis of Two Distributions
     14.5 Linear Correlation
     14.6 Nonparametric or Rank Correlation
     14.7 Do Two-Dimensional Distributions Differ?
     14.8 Savitzky-Golay Smoothing Filters
     15 Modeling of Data
     15.0 Introduction
     15.1 Least Squares as a Maximum Likelihood Estimator
     15.2 Fitting Data to a Straight Line
     15.3 Straight-Line Data with Errors in Both Coordinates
     15.4 General Linear Least Squares
     15.5 Nonlinear Models
     15.6 Confidence Limits on Estimated Model Parameters
     15.7 Robust Estimation
16 Integration of Ordinary Differential Equations
     l6.0 Introduction
     16.1 Runge-Kutta Method
     16.2 Adaptive Stepsize Control for Runge-Kutta
     16.3 Modified Midpoint Mealod
     16.4 Richardson Extrapolation and the Bulirsch-Stoer Method
     16.5 Second-Order Conservative Equations
     16.6 Stiff Sets of Equations
     l6.7 Multistep, Multivalue, and Predictor-Corrector Methods
     17 Two Point Boundary Value Problems
     17.O Introdnction
     17.1 The Shooting Method
     17.2 Shooting to a Fitting Point
     17.3 Relaxation Methods
     17.4 A Worked Example: Spheroidal Harmonics
     17.5 Automated Allocation of Mesh Points
     17.6 Handling Internal Boundary Conditions or Singular Points
     18 Integral Equations and Inverse Theory
     18.0 Introduction
     18.1 Fredholm Equations of the Second Kind
     18.2 Volterra Equations
     18.3 Integral Equations with Singular Kernels
     18.4 Inverse Problems and the Use of A Priori Information
     18.5 Linear Regularization Methods
     18.6 Backus-Gilbert Method
     18.7 Maximum Entropy Image Restoration
     19 Partial Differential Equations
     19.0 Introduction
     19.1 Flux-Conservative Initial Value Problems
     19.2 Diffusive Initial Value Problems
     19.3 Initial Value Problems in Multidimensions
     19.4 Fourier and Cyclic Reduction Methods for Boundary
     Value Problems
     19.5 Relaxation Methods for Boundary Value Problems
     19.6 Multigrid Methods for Boundary Value Problems
     20 Less-Numerical Algorithms
     20.0 Introduction
     20.1 Diagnosing Machine Parameters
     20.2 Gray Codes
     20.3 Cyclic Redundancy and Other Checksums
     20.4 Huffman Coding and Compression of Data
     20.5 Arithmetic Coding
     20.6 Arithmetic at Arbitrary Precision
     References
     Appendix A: Table of Funetion Declarations
     Appendix B: Utility Routines and Classes
     Appendix C: Convetring to Single Precision
     Index of Programs and Dependencies
     General Index

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读者：beyondma 最新讨论：2007-10-11 7:59:17 评价等级：
优秀的程序员用C++,读过这本书实在是感觉茅塞顿开.循续渐近的讲解C++的相关知识.
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