Numerical Recipes 3rd Edition: The Art of Scientific Computing

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  • Edition: 3rd
  • Format: Hardcover
  • Copyright: 2007-09-10
  • Publisher: Cambridge University Press
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The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a comprehensive text and reference work on scientific computing. Thoroughly self-contained, it proceeds from mathematical and theoretical considerations to actual, practical computer routines. This new version incorporates completely new C++ versions of the more than 300 Numerical Recipes Second Edition routines widely recognized as the most accessible and practical basis for scientific computing, in addition to including the full mathematical and explanatory contents of Numerical Recipes in C. Key Features: Includes linear algebra, interpolation, special functions, random numbers, nonlinear sets of equations, optimization, eigensystems, Fourier methods and wavelets, statistical tests, ODEs and PDEs, integral equations, and inverse theory. A wealth of tricks and tips for scientific computing in C++ The routines, in ANSI/ISO C++ source code, can be used with almost any existing C++ vector/matrix class library, according to user preference Includes a simple class library for stand-alone use Other new Numerical Recipes products for your library... Numerical Recipes Example Book [C++] Numerical Recipes Code CDROM with Windows, DOS, or Macintosh Single Screen License--v2.10 including C++, Second Edition Numerical Recipes Code CDROM with LINUX or UNIX Single Screen License v2.10 including C++, Second Edition Numerical Recipes Code CDROM with Windows, DOS, or Macintosh Single Screen License

Table of Contents

Solution of linear algebraic equations
Interpolation and extrapolation
Integration of functions
Evaluation of functions
Special functions
Random numbers
Sorting and selection
Root finding and nonlinear sets of equations
Minimization or maximization of functions
Fast Fourier transform
Fourier and spectral applications
Statistical description of data
Modeling of data
Classification and inference
Integration of ordinary differential equations
Two point boundary value problems
Integral equations and inverse theory
Partial differential equations
Computational geometry
Less-numerical algorithms
Table of Contents provided by Publisher. All Rights Reserved.

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