A completely positive representation of 0-1 linear programs with joint probabilistic constraints

Jianqiang Cheng, Abdel Lisser

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this paper, we study 0-1 linear programs with joint probabilistic constraints. The constraint matrix vector rows are assumed to be independent, and the coefficients to be normally distributed. Our main results show that this non-convex problem can be approximated by a convex completely positive problem. Moreover, we show that the optimal values of the latter converge to the optimal values of the original problem. Examples randomly generated highlight the efficiency of our approach.

Original languageEnglish (US)
Pages (from-to)597-601
Number of pages5
JournalOperations Research Letters
Volume41
Issue number6
DOIs
StatePublished - 2013
Externally publishedYes

Keywords

  • 0-1 linear program
  • Completely positive problems
  • Joint probabilistic constraints
  • Semidefinite programming
  • Stochastic programming

ASJC Scopus subject areas

  • Software
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

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