A Computational Study of Active Set Strategies in Nonlinear Programming with Linear Constraints.

Abstract

An essential part of many iterative methods for linearly constrained nonlinear programming problems is a procedure for determining those inequality constraints which will be 'active' (that is, satisfied as equalities) at each iteration. The author discusses experiments in which he used several strategies for identifying active constraints in conjunction with two well-known algorithms for linearly constrained optimization. The results indicate that in most cases a strategy which keeps the number of constraints in the active set as small as possible is computationally most efficient.

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1975
Accession Number
ADA020202

Entities

People

  • Melanie L. Lenard

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Evolutionary Algorithms
  • Heuristic Methods
  • Inequalities
  • Iterations
  • Mathematical Programming
  • Mathematics
  • Nonlinear Programming
  • Optimization

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.