A Sequential Quadratic Programming Algorithm Using An Incomplete Solution of the Subproblem

Abstract

We analyze sequential quadratic programming (SQP) methods to solve nonlinear constrained optimization problems that are more flexible in their definition than standard SQP methods. The type of flexibility introduced is motivated by the necessity to deviate from the standard approach when solving large problems. Specifically we no longer require a minimizer of the QP subproblem to be determined or particular Lagrange multiplier estimates to be used. Our main focus is on an SQP algorithm that uses a particular augmented Lagrangian merit function. New results are derived for this algorithm under weaker conditions than previously assumed; in particular, it is not assumed that the iterates lie on a compact set.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1993
Accession Number
ADA267216

Entities

People

  • Francisco J. Prieto
  • Walter Murray

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Fluid Dynamics
  • Computer Programming
  • Evolutionary Algorithms
  • Lagrangian Functions
  • Mathematical Programming
  • New York
  • Nonlinear Programming
  • Notation
  • Numerical Analysis
  • Operations Research
  • Optimization
  • Quadratic Programming
  • Resilience
  • Sequences
  • Square Roots
  • Standards

Fields of Study

  • Mathematics

Readers

  • Operations Research