On the Convergence of a Sequential Quadratic Programming Method with an Augmented Lagrangian Line Search Functions.

Abstract

Sequential quadratic programming methods as developed by Wilson, Han, and Powell have gained considerable attention in the last few years mainly because of their outstanding numerical performance. Although the theoretical convergence aspects of this method and its various modifications have been investigated in the literature, there still remain some open questions which will be treated in this paper. The convergence theory to be presented, takes into account the additional variable introduced in the quadratic programming subproblem to avoid inconsistency, the one-dimensional minimization procedure, and, in particular, and 'active set' strategy to avoid the recalculation of unnecessary gradients. This paper also contains a detailed mathematical description of a nonlinear programming algorithm which has been implemented by the author.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1982
Accession Number
ADA115667

Entities

People

  • Klaus Schittkowski

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Business Administration
  • Computer Programming
  • Convergence
  • Evolutionary Algorithms
  • Iterations
  • Mathematical Programming
  • Mathematics
  • Military Research
  • New York
  • Nonlinear Programming
  • Numerical Analysis
  • Operations Research
  • Optimization
  • Quadratic Programming
  • United States

Fields of Study

  • Mathematics

Readers

  • Operations Research
  • Systems Analysis and Design