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

Abstract

A feature of current sequential quadratic programming (SQP) methods to solve nonlinear constrained optimization problems is the necessity at each iteration to solve a quadratic program (QP). We show that if the QP subproblem is convex and an active-set method is used to solve it, then there exist iterates other than the minimizer that may be used to define a suitable search direction. None of the usual properties of an SQP method are compromised by the new definition of the search direction. We derive some new properties for an SQP method that uses a particular augmented Lagrangian merit function. Specifically we show, under suitable additional assumptions, that the rate of convergence is superlinear. We also show that the penalty parameter used in the merit function is bounded. (KR)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1990
Accession Number
ADA228829

Entities

People

  • Francisco J. Prieto
  • Walter Murray

Organizations

  • Stanford University

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Convergence
  • Evolutionary Algorithms
  • Iterations
  • Mathematical Programming
  • New York
  • Nonlinear Programming
  • Notation
  • Numerical Analysis
  • Operations Research
  • Optimization
  • Quadratic Programming
  • Sequences
  • Square Roots
  • Test And Evaluation
  • Test Sets

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Database Systems and Applications
  • Linear Algebra