数値計算のレシピソースコード(第3版)<br>Numerical Recipes : The Art of Scientific Computing (3TH)

数値計算のレシピソースコード(第3版)
Numerical Recipes : The Art of Scientific Computing (3TH)

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  • 製本 Hardcover:ハードカバー版/ページ数 1235 p.
  • 言語 ENG
  • 商品コード 9780521880688
  • DDC分類 518.0285

基本説明

ベストセラーテキスト5年振りの改訂版。
The power of Numerical Recipes is now available to C++ users. Completely self-contained and fully compliant with the new ANSI/ISO C++ standard, the book features more than 300 supreme routines that provide a rock-solid basis for Scientific Computing of every kind.

Full Description


Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in colour for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs; a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres; interior point methods for linear programming; MCMC; an expanded treatment of ODEs with completely new routines; and many new statistical distributions. For support, or to subscribe to an online version, please visit www.nr.com.

Contents

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 spectral 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; References.