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

DOI10.1515/IJNSNS.2001.2.2.139zbMath1116.90387MaRDI QIDQ4674254

Irwin S. Pressman, Shafiu Jibrin

Publication date: 9 May 2005

Published in: International Journal of Nonlinear Sciences and Numerical Simulation (Search for Journal in Brave)

Mathematics Subject Classification ID

Semidefinite programming (90C22) Monte Carlo methods (65C05)

Related Items (3)

Semidefinite diagonal directions Monte Carlo algorithms for detecting necessary linear matrix inequality constraints ⋮ Identifying redundant linear constraints in systems of linear matrix inequality constraints ⋮ Detecting redundancy in optimization problems over intersection of ellipsoids

Uses Software

• Sdpsol
• EISPACK

Cites Work

• Some applications of optimization in matrix theory
• On the best case performance of hit and run methods for detecting necessary constraints
• On Trivial and Binding Constraints in Programming Problems
• Bayesian stopping rules for multistart global optimization methods
• Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed over Bounded Regions
• Primal--Dual Path-Following Algorithms for Semidefinite Programming
• Determinant Maximization with Linear Matrix Inequality Constraints
• Interior Point Methods in Semidefinite Programming with Applications to Combinatorial Optimization
• Semidefinite Programming
• Solving Large-Scale Sparse Semidefinite Programs for Combinatorial Optimization

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