Chance constrained 0–1 quadratic programs using copulas

Jianqiang Cheng, Michal Houda, Abdel Lisser

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

In this paper, we study 0–1 quadratic programs with joint probabilistic constraints. The row vectors of the constraint matrix are assumed to be normally distributed but are not supposed to be independent. We propose a mixed integer linear reformulation and provide an efficient semidefinite relaxation of the original problem. The dependence of the random vectors is handled by the means of copulas. Finally, numerical experiments are conducted to show the strength of our approach.

Original languageEnglish (US)
Pages (from-to)1283-1295
Number of pages13
JournalOptimization Letters
Volume9
Issue number7
DOIs
StatePublished - Oct 22 2015
Externally publishedYes

Keywords

  • 0–1 quadratic program
  • Copula theory
  • Joint probabilistic constraints
  • Semidefinite programming
  • Stochastic programming

ASJC Scopus subject areas

  • Control and Optimization

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