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

Shafiu Jibrin; Irwin S. Pressman

doi:10.1515/IJNSNS.2001.2.2.139

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

Shafiu Jibrin, Irwin S. Pressman

Mathematics and Statistics

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Pages (from-to)	139-153
Number of pages	15
Journal	International Journal of Nonlinear Sciences and Numerical Simulation
Volume	2
Issue number	2
DOIs	https://doi.org/10.1515/IJNSNS.2001.2.2.139
State	Published - 2001

ASJC Scopus subject areas

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

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10.1515/IJNSNS.2001.2.2.139

Cite this

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title = "Monte Carlo Algorithms for the Detection of Necessary Linear Matrix Inequality Constraints",

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.",

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AB - 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.

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JO - International Journal of Nonlinear Sciences and Numerical Simulation

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

Abstract

ASJC Scopus subject areas

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