An SQP algorithm for extended linear-quadratic problems in stochastic programming

Liqun Qi, Robert S. Womersley

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)

Abstract

Extended Linear-Quadratic Programming (ELQP) problems were introduced by Rockafellar and Wets for various models in stochastic programming and multistage optimization. Several numerical methods with linear convergence rates have been developed for solving fully quadratic ELQP problems, where the primal and dual coefficient matrices are positive definite. We present a two-stage sequential quadratic programming (SQP) method for solving ELQP problems arising in stochastic programming. The first stage algorithm realizes global convergence and the second stage algorithm realizes superlinear local convergence under a condition called B-regularity. B-regularity is milder than the fully quadratic condition; the primal coefficient matrix need not be positive definite. Numerical tests are given to demonstrate the efficiency of the algorithm. Solution properties of the ELQP problem under B-regularity are also discussed. Baltzer AG, Science Publishers.
Original languageEnglish
Pages (from-to)251-285
Number of pages35
JournalAnnals of Operations Research
Volume56
Issue number1
DOIs
Publication statusPublished - 1 Dec 1995
Externally publishedYes

Keywords

  • B-regularity
  • convergence
  • quadratic programming
  • Stochastic programming

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Decision Sciences(all)

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