Optimization for Simulation : Theory vs . Practice

By R. H. Smith. 2002

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Citations in the corpus

Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming (2013, background): “· • Finally, in simulation-based optimization , the objective function is given by f where F ( · , ξ ) is not given explicitly, but through a black-box simulation procedure (e.g., [1, 7]).”
Accelerated gradient methods for nonconvex nonlinear and stochastic programming (2013, background): “Third, the convexity of some objective functions represented by a black-box procedure is usually unknown, e.g., in simulation-based optimization [1,9, 2,18].”
Simulation optimization: a review, new developments, and applications (2005, methodology): “Comparing with Table 1 in Fu (2002), one observes that ProModel and SIMUL8 have both migrated to OptQuest from their previous simulation optimization packages (SimRunner and OPTIMIZ, respectively).”
Simulation optimization: a review of algorithms and applications (2014, methodology): “Detailed books on these methods are available in the literature [ 85, 69, 172, 156, 65] and more high-level descriptions are available in papers [194, 60].”
A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems (2015, background): “Several works in the literature discuss both EF and SMC methods [22–24].”
Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization (2013, background): “For some examples which ﬁt our setting, one may refer the problems in references [1,11,12,15,16,21,22,23,32].”
A unified framework for stochastic optimization (2019, background): “Powell / European Journal of Operational Research 0 0 0 (2018) 1–27 5 ARTICLE IN PRESS JID: EOR [m5G; August 14, 2018;23:35 ]”
Selecting the best system when systems are revealed sequentially (2007, methodology): “Another instance is optimization via simulation (see Fu (2002) for a thorough review).”