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We consider a variation of the subset selection problem in ranking and selection, where motivated by recently developed global optimization approaches applied to simulation optimization, our objective is to identify the top-m out of k designs based on simulated output. Using the optimal computing budget framework, we formulate the problem as that of(More)
" Finite-dimensional regulators for a class of infinite-dimensional systems, " Syst. [13] Q. Vu, " The operator equation AX 0 XB = C with unbounded operators A and B and related abstract Cauchy problems, " Mathematische Abstract—We propose a novel algorithm called evolutionary policy iteration (EPI) for solving infinite horizon discounted reward Markov(More)
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effective for high-dimensional simulation optimization problems. The main idea is to estimate the gradient using simulation output performance measures at only <i>two</i> settings of the <i>N</i>-dimensional parameter vector being optimized rather than at the(More)
The integration of optimization and simulation has become nearly ubiquitous in practice, as most discrete-event simulation packages now include some type of optimization routine. This panel session's objective was to explore the present state of the art in simulation optimization, prevailing issues for researchers, and future prospects for the field. The(More)
Based on recent results for multiarmed bandit problems, we propose an adaptive sampling algorithm that approximates the optimal value of a finite-horizon Markov decision process (MDP) with finite state and action spaces. The algorithm adaptively chooses which action to sample as the sampling process proceeds and generates an asymptotically unbiased(More)