Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
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In this paper, we propose speed-up techniques for computation of the Markov chain model to find an optimal batting order in a baseball team. The proposed technique parallelizes computation of the Markov chain model for batting orders, where probabilities to obtain scores by the batting orders are computed using the D'Esopo and Lefkowitz model, on the Grid. In addition, the proposed technique improves the performance by sharing parameters about batting orders. On a Grid environment, load balancing is appropriately performed considering performances of computing resources. The experimental results show that the proposed technique finds the optimal batting order in 27,216,000 batting orders for 3,278 seconds on the Grid testbed.