A sampling method to chance-constrained semidefinite optimization

Chuan Xu, Jianqiang Cheng, Abdel Lisser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Semidefinite programming has been widely studied for the last two decades. Semidefinite programs are linear programs with semidefinite constraint generally studied with deterministic data. In this paper, we deal with a stochastic semidefinte programs with chance constraints, which is a generalization of chance-constrained linear programs. Based on existing theoretical results, we develop a new sampling method to solve these chance constraints semidefinite problems. Numerical experiments are conducted to compare our results with the state-of-the-art and to show the strength of the sampling method.

Original languageEnglish (US)
Title of host publicationICORES 2015 - 4th International Conference on Operations Research and Enterprise Systems, Proceedings
EditorsBegona Vitoriano, Greg H. Parlier
PublisherSciTePress
Pages75-81
Number of pages7
ISBN (Electronic)9789897580758
DOIs
StatePublished - 2015
Externally publishedYes
Event4th International Conference on Operations Research and Enterprise Systems, ICORES 2015 - Lisbon, Portugal
Duration: Jan 10 2015Jan 12 2015

Publication series

NameICORES 2015 - 4th International Conference on Operations Research and Enterprise Systems, Proceedings

Conference

Conference4th International Conference on Operations Research and Enterprise Systems, ICORES 2015
Country/TerritoryPortugal
CityLisbon
Period1/10/151/12/15

Keywords

  • Chance-constrained Programming
  • Sample Approximation
  • Semidefinite Program
  • Stochastic Programming

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Control and Systems Engineering

Fingerprint

Dive into the research topics of 'A sampling method to chance-constrained semidefinite optimization'. Together they form a unique fingerprint.

Cite this