Stochastic semidefinite optimization using sampling methods

Chuan Xu, Jianqiang Cheng, Abdel Lisser

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

Abstract

This paper deals with stochastic semidefinite chance constrained problems. Semidefinite optimization generalizes linear programs, and generally solves deterministic optimization. We propose a new sampling method to solve chance constrained semidefinite optimization problems. Numerical results are given in order to compare the performances of our approach to the state-of-the-art.

Original languageEnglish (US)
Title of host publicationOperations Research and Enterprise Systems - 4th International Conference, ICORES 2015, Revised Selected Papers
EditorsDominique de Werra, Begoña Vitoriano, Greg H. Parlier
PublisherSpringer-Verlag
Pages93-103
Number of pages11
ISBN (Print)9783319276793
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

NameCommunications in Computer and Information Science
Volume577
ISSN (Print)1865-0929

Conference

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

Keywords

  • Chance-constrained programming
  • Linear matrix inequalities
  • Sample approximation
  • Semidefinite program
  • Stochastic programming

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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