Decomposition based interior point methods for two-stage stochastic convex quadratic programs with recourse

Sanjay Mehrotra*, M. Gokhan Ozevin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Zhao showed that the log barrier associated with the recourse function of two-stage stochastic linear programs behaves as a strongly self-concordant barrier and forms a self-concordant family on the first-stage solutions. In this paper, we show that the recourse function is also strongly self-concordant and forms a self-concordant family for the two-stage stochastic convex quadratic programs with recourse. This allows us to develop Bender's decomposition based linearly convergent interior point algorithms. An analysis of such an algorithm is given in this paper.

Original languageEnglish (US)
Pages (from-to)964-974
Number of pages11
JournalOperations Research
Volume57
Issue number4
DOIs
StatePublished - Jul 1 2009

Keywords

  • Bender's decomposition
  • Large-scale optimization
  • Linear-quadratic programming
  • Nondifferentiable convex optimization
  • Two-stage stochastic programming

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research

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