QP-Based Methods for Large-Scale Nonlinearly Constrained Optimization.

Abstract

Several methods for nonlinearly constrained optimization have been suggested in recent years that are based on solving a quadratic programming (QP) subproblem to determine the direction of search. Even for dense problems, there is no consensus at present concerning the 'best' formulation of the QP subproblem. When solving large problems, many of the options possible for small problems become unreasonably expensive in terms of storage and/or arithmetic operations. This paper discusses the inherent difficulties of developing QP-based methods for large-scale nonlinearly constrained optimization, and suggests some possible approaches. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1981
Accession Number
ADA097407

Entities

People

  • Margaret H. Wright
  • Michael Saunders
  • Philip Edward Gill
  • Walter Murray

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Arithmetic
  • Computational Fluid Dynamics
  • Computations
  • Computer Programming
  • Contracts
  • Efficiency
  • Equations
  • Evolutionary Algorithms
  • Inequalities
  • Linear Programming
  • Military Research
  • Nonlinear Programming
  • Operations Research
  • Optimization
  • Quadratic Programming
  • Simplex Method

Readers

  • Operations Research
  • Systems Analysis and Design