Compact Numerical Methods for Computers: Linear Algebra and Function MinimisationWiley, 1979 - 227 pages This second edition of Compact Numerical Methods for Computers presents reliable yet compact algorithms for computational problems. As in the previous edition, the author considers specific mathematical problems of wide applicability, develops approaches to a solution and the consequent algorithm, and provides the program steps. He emphasizes useful applicable methods from various scientific research fields, ranging from mathematical physics to commodity production modeling. While the ubiquitous personal computer is the particular focus, the methods have been implemented on computers as small as a programmable pocket calculator and as large as a highly parallel supercomputer. New to the Second Edition The accompanying software (available by coupon at no charge) includes not only the algorithm source codes, but also driver programs, example data, and several utility codes to help in the software engineering of end-user programs. The codes are designed for rapid implementation and reliable use in a wide variety of computing environments. Scientists, statisticians, engineers, and economists who prepare/modify programs for use in their work will find this resource invaluable. Moreover, since little previous training in numerical analysis is required, the book can also be used as a supplementary text for courses on numerical methods and mathematical software. |
Table des matières
A STARTING POINT | 1 |
FORMAL PROBLEMS IN LINEAR ALGEBRA | 14 |
THE SINGULARVALUE DECOMPOSITION AND ITS | 25 |
Droits d'auteur | |
22 autres sections non affichées
Autres éditions - Tout afficher
Compact Numerical Methods for Computers: Linear Algebra and Function ... John C. Nash Affichage d'extraits - 1990 |
Compact Numerical Methods for Computers: Linear Algebra and Function ... John C. Nash Affichage d'extraits - 1979 |
Expressions et termes fréquents
algorithm 14 approximation array axial search b₁ b₂ back-substitution calculation Choleski decomposition coefficient column components Compute P=S(b conjugate gradients algorithm conjugate gradients method constraints convergence test Data General NOVA derivatives diagonal elements digit eigenproblem eigensolutions eigenvalue problem eigenvectors End loop Enter EVALNS evaluations Example formula function S(b function value Gauss elimination Gauss-Jordan generalised given gives goto step 11 initialise instance interval inverse interpolation inverse iteration ITNS Jacobi algorithm lambda least-squares problem Let b[i Let count linear equations linear search machine precision Marquardt method minimisation problem minimum Nelder-Mead norm normalised Note orthogonal orthogonalisation performed pivoting positive definite possible procedure quadratic Rayleigh quotient reduce REDUCTION-H restart root root-finding S=AC search directions simplex singular values singular-value decomposition solve steepest descents STEP DESCRIPTION step length sum of squares symmetric matrix tolerance unit matrix variable metric algorithm vector vertex Wilkinson zero