ISBN-10:
085274319X
ISBN-13:
9780852743195
Pub. Date:
01/01/1990
Publisher:
Taylor & Francis
Compact Numerical Methods For Computers / Edition 2

Compact Numerical Methods For Computers / Edition 2

by John C Nash, J C Nash

Paperback

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Overview

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

  • Presents program steps as Turbo Pascal code
  • Includes more algorithmic examples
  • Contains an extended bibliography

    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.
  • Product Details

    ISBN-13: 9780852743195
    Publisher: Taylor & Francis
    Publication date: 01/01/1990
    Edition description: 2ND
    Pages: 278
    Product dimensions: 6.12(w) x 9.25(h) x 0.61(d)

    Table of Contents

    A starting point
    Formal problems in linear algebra
    The singular-value decomposition and its use to solve least-squares problems
    Handling larger problems
    Some comments on the formation of the cross-product matrix ATA
    Linear equations-a direct approach
    The Choleski decomposition
    The symmetric positive definite matrix again
    The algebraic eigenvalue generalized problem
    Real symmetric matrices
    The generalized symmetric matrix eigenvalue problem
    Optimization and nonlinear equations
    One-dimensional problems
    Direct search methods
    Descent to a minimum I-variable metric algorithms
    Descent to a minimum II-conjugate gradients
    Minimizing a nonlinear sum of squares
    Leftovers
    The conjugate gradients method applied to problems in linear algebra
    Appendices
    Bibliography
    Index

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