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A theoretical treatment of Monte Carlo optimization--simulation using perturbation analysis, adaptive methods, and variance reduction techniques. Emphasizes concepts rather than mathematical completeness. Shows how to use simulation and Monte Carlo methods efficiently for estimating performance measures, sensitivities and optimization of stochastic systems.
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