Информатика и вычислительная техника
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  • добавлен 07 апреля 2009 г.
Numerical Recipes. The Art of Scientific Computing. 3rd Edition, 2007
Co-authored by four leading scientists from academia and industry, Numerical Recipes Third Edition starts with basic mathematics and computer science and proceeds to complete, working routines. Widely recognized as the most comprehensive, accessible and practical basis for scientific computing, this new edition incorporates more than 400 Numerical Recipes routines, many of them new or upgraded. The executable C++ code, now printed in color for easy reading, adopts an object-oriented style particularly suited to scientific applications. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. New key features:
- 2 new chapters, 25 new sections, 25% longer than Second Edition
- Thorough upgrades throughout the text
- Over 100 completely new routines and upgrades of many more.
- New Classification and Inference chapter, including Gaussian mixture models, HMMs, hierarchical clustering, Support Vector Machines
- New Computational Geometry chapter covers KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres New sections include interior point methods for linear programming, Monte Carlo Markov Chains, spectral and pseudospectral methods for PDEs, and many new statistical distributions
An expanded treatment of ODEs with completely new routines
- Plus comprehensive coverage of 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.

1. Preliminaries.
2. Solution of Linear Algebraic Equations.
3. Interpolation and Extrapolation.
4. Integration of functions.
5. Evaluation of Functions.
6. Special Functions.
7. Random Numbers.
8. Sorting and Selection.
9. Root Finding and Nonlinear Sets of Equations.
10. Minimization or Maximization of Functions.
11. Eigensystems.
12. Fast Fourier Transform.
13. Fourier and Spectrum Applications.
14. Statistical Description of Data.
15. Modeling of Data.
16. Classification and Inference.
17. Integration of Ordinary Differential Equations.
18. Two-Point Boundary Value Problems.
19. Integral Equations and Inverse Theory.
20. Partial Differential Equations.
21. Computational Geometry.
22. Less-Numerical Algorithms.
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