Monte Carlo Algorithms for the Detection of Necessary Linear Matrix Inequality Constraints

Shafiu Jibrin, Irwin S. Pressman

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

We reduce the size of large semidefinite programming problems by identifying necessary linear matrix inequalities (LMI's) using Monte Carlo techniques. We describe three algorithms for detecting necessary LMI constraints that extend algorithms used in linear programming to semidefinite programming. We demonstrate that they are beneficial and could serve as tools for a semidefinite programming preprocessor.

Original languageEnglish (US)
Pages (from-to)139-153
Number of pages15
JournalInternational Journal of Nonlinear Sciences and Numerical Simulation
Volume2
Issue number2
DOIs
StatePublished - Jan 1 2001

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Computational Mechanics
  • Modeling and Simulation
  • Engineering (miscellaneous)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Monte Carlo Algorithms for the Detection of Necessary Linear Matrix Inequality Constraints'. Together they form a unique fingerprint.

  • Cite this