Algorithms for Nonlinear Programming.

Abstract

Several algorithms in linear, quadratic, and nonlinear programming have been developed and analyzed. These include: (i) The development of relaxation methods for finding a feasible solution to a system of linear inequalities based upon generating surrgate constraints; (ii) The worst-case behaviour of the shadow-vertex simplex algorithm was shown to be the exponential; (iii) Necessary and sufficient conditions for versions of iterative methods, including the Jacobi, Gauss-Seidel and SOR methods, designed for solving equality constrained quadratic programs have been obtained; (iv) The development of numerically stable and efficient implementations of primal methods for quadratic programming; and (v) the development of optimal algorithms for estimating Jacobian and Hessian matrices arising in finite difference calculations. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1983
Accession Number
ADA130903

Entities

People

  • Donald Goldfarb

Organizations

  • City College of New York

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computer Programming
  • Computer Science
  • Differential Equations
  • Evolutionary Algorithms
  • Inequalities
  • Linear Programming
  • Mathematical Programming
  • Military Research
  • New York
  • Nonlinear Programming
  • Partial Differential Equations
  • Quadratic Programming
  • Simplex Method
  • Universities

Fields of Study

  • Mathematics

Readers

  • Operations Research