Optimization for Simulation: Theory vs. Practice

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A survey on metaheuristics for stochastic combinatorial optimization (2009, background): “Interestingly, as noted by Fu (2002), the many new commercial software packages for simulation do not take advantage of the theoretical results of the literature.”
Learning surrogate models for simulation‐based optimization (2014, unknown): “2 2 2 [2, 2] 2 2 [6, 8] [6, 11] [12, 15] [12, 15] 2 3 3 3 3 3 [5, 12] [5, 10] [12, 14] [12, 14] 2 4 [3, 4] [3, 4] [3, 4] [3, 4] [8, 11] [8, 10] [11, 12] [11, 12] 2 5 [2, 4] [2, 4] [2, 5] [2, 5] [3, 12] [4, 11] [10, 16] [10, 16] 2 6 [5, 6] [6, 6] [5, 6] [6, 6] [7, 10] [6, 7] [11, 13] [11, 13] 2 7 [4,”
Optimization Methods for Large-Scale Machine Learning (2016, background): “ntioned here have analogues in the simulation and stochastic optimization communities, where they are referred to as stochastic approximation (SA) and sample average approximation (SAA), respectively [63]. Inset 3.1: Herbert Robbins and Stochastic Approximation The paper by Robbins and Monro [130] r”