Titlebook: Handbook of Simulation Optimization; Michael C Fu Book 2015 Springer Science+Business Media New York 2015 Markov.Monte Carlo.Operations Ma

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書目名稱Handbook of Simulation Optimization影響因子(影響力)




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Cabinet

Stochastic Gradient Estimation, stochastic approximation and sample average approximation. We begin by describing approaches based on finite differences, including the simultaneous perturbation method. The remainder of the chapter then focuses on the direct gradient estimation techniques of perturbation analysis, the likelihood r

DEVIL

An Overview of Stochastic Approximation,thm that can be viewed as the stochastic counterpart to steepest descent in deterministic optimization. We begin with the classical methods of Robbins–Monro (RM) and Kiefer–Wolfowitz (KW). We discuss the challenges in implementing SA algorithms and present some of the most well-known variants such a

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Stochastic Constraints and Variance Reduction Techniques,and variance reduction techniques. While Monte Carlo simulation-based methods have been successfully used for stochastic optimization problems with deterministic constraints, there is a growing body of work on its use for problems with stochastic constraints. The presence of stochastic constraints b

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A Review of Random Search Methods,system performance is estimated via simulation. Next, we discuss methods for solving simulation optimization problems with discrete decision variables and one (stochastic) performance measure, with emphasis on simulated annealing. Finally, we expand our scope to address simulation optimization probl

天然熱噴泉

Stochastic Adaptive Search Methods: Theory and Implementation,on quickly. One drawback is that strong convergence results to a global optimum require strong assumptions on the structure of the problem..This chapter begins by discussing optimization formulations for simulation optimization that combines . performance with a measure of ., or risk. It then summar