MINIMIZING STRUCTURED UNCONSTRAINED FUNCTIONS.

Abstract

For the general nonlinear programming problem (nonlinear constraints, nonlinear objective function) the difficulty of obtaining a solution is controlled by n, the number of problem variables. This is in contrast to linear programming (where nonnegativity requirements are always assumed), where m, the number of nontrivial constraints, determines the size of the problems that can be handled and their speed of solution. This paper outlines in detail modifications to the sequential unconstrained minimization technique (SUMT) for nonlinear programming that expand the size of problems that it can solve to the limitations imposed by the special-case algorithms devised for them. In particular, the special cases of mathematical programming, i.e., linear, separable, and 'factorable,' programming, will be described. (Author)

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1967
Accession Number
AD0661649

Entities

People

  • Garth P. Mccormick

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Computer Programming
  • Contrast
  • Evolutionary Algorithms
  • Heuristic Methods
  • Interdisciplinary Science
  • Linear Programming
  • Mathematical Programming
  • Mathematics
  • Nonlinear Programming
  • Operations Research
  • Systems Science

Fields of Study

  • Mathematics

Readers

  • Operations Research