Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method

By C. Leake. 1994

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Citations in the corpus

Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming (2013, methodology): “Other techniques have also been developed to compute unbiased estimators for the gradient of f ( ) in (1.5) (see, e.g., [8, 13, 20, 35]).”
Lectures on Stochastic Programming: Modeling and Theory (2009, methodology): “Variants of this approach are known as the stochastic counterpart method (Rubinstein and Shapiro [220],[221]) and sample-path optimization (Plambeck, Fu, Robinson and Suri [187] and Robinson [209]), for example.”
Monte Carlo Gradient Estimation in Machine Learning (2019, background): “The framework of Schulman et al. (2015), Kucukelbir et al.”
A Guide to Sample Average Approximation (2015, methodology): “…in statistics, but perhaps the strongest roots of the idea from an Operations Research perspective lie in variants called the stochastic counterpart method [Rubinstein and Shapiro, 1990, 1993] and sample-path optimization [Plambeck et al., 1996, Robinson, 1996, Healy and Schruben, 1991].”
Simulation optimization: a review of algorithms and applications (2014, background): “Several papers [172, 37, 77, 180, 46] discuss convergence results and algorithms in this context.”
Monte Carlo sampling-based methods for stochastic optimization (2014, methodology): “For details on this result, as well as an extension to the constrained case, we refer to King and Rockafellar [133], Shapiro [217], and Rubinstein and Shapiro [210].”
Conditional Value-at-Risk for General Loss Distributions (2002, unknown): “…of Washington, Department of Mathematics, Box 354350, Seattle, WA 98195-4350; e-mail: rtr@math.washington.edu 2University of Florida, Department of Industrial and Systems Engineering, PO Box 116595, Gainesville, FL 32611-6595; e-mail: uryasev@ise.u .edu; URL: http://www.ise.u .edu/uryasev”
Simulation optimization (2001, background): “Q : What is simulation optimization Or at least what do we mean by the term in the context of this tutorial”
Simulation optimization: a review, new developments, and applications (2005, background): “Sample path optimization (also known as stochastic counterpart, sample average approximation; see Rubinstein and Shapiro 1993) takes many simulations ﬁrst, and then tries to optimize the resulting estimates.”
Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems with Correlated Random Variables (2011, methodology): “For the derivation of the sensitivity, the following four assumptions, which are known as the regularity conditions, are required [28,29].”
On the asymptotics of constrained local M-estimators (2000, background): “The uniform version (3.4) of the law of large numbers can be proved then under some mild additional conditions [see, e.g., Rubinstein and Shapiro (1993), Section 2.6, for an elementary discussion].”
Stochastic Optimization (2003, background): “A well developed statistical inference of the estimators v̂ N and x̂ N exists (Rubinstein and Shapiro 1993).”
Extensions of Stochastic Optimization Results to Problems with System Failure Probability Functions (2007, background): “[21] for a proof which holds under weaker assumptions than those stated here.”
Handbook of Simulation Optimization (2014, methodology): “Many reviews on R&S are available [1, 29, 39].”
Stochastic sensitivity analysis by dimensional decomposition and score functions (2009, methodology): “The two remainingmethods, the infinitesimal perturbation analysis [6,7] and the score functionmethod [8], have been mostly viewed as competing methods, where both performance and sensitivities can be obtained from a single stochastic simulation.”
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].”
Optimization of computer simulation models with rare events (1997, methodology): “[26] R.Y. Rubinstein and A. Shapiro, Discrete Event Systems: Sensitivity Analysis andStochastic Optimization via the Score Function Method (John Wiley & Sons, 1993).”