Branch-and-Cut Algorithms for the Bilinear Matrix Inequality Eigenvalue Problem
Computational Optimization and Applications
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GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
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HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
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JGI '02 Proceedings of the 2002 joint ACM-ISCOPE conference on Java Grande
P2P design and implementation of a parallel branch and bound algorithm for grids
International Journal of Grid and Utility Computing
ISSADS'05 Proceedings of the 5th international conference on Advanced Distributed Systems
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The BMI Eigenvalue Problem is one of optimization problems and is to minimize the greatest eigenvalue of a bilinear matrix function. This paper proposes a parallel algorithm to compute the 驴-optimal solution of the BMI Eigenvalue Problem on parallel and distributed computing systems. The proposed algorithm performs a parallel branch and bound method to compute the 驴-optimal solution using the Master-Worker paradigm. The performance evaluation results on PC clusters and a Grid computing system showed that the proposed algorithm reduced computation time of the BMI Eigenvalue problem to 1/91 of the sequential computation time on a PC cluster with 128CPUs and reduced that to 1/7 on a Grid computing system. The results also showed that tuning of the computational granularity on a worker was required to achieve the best performance on a Grid computing system.