- On Sampling Rates in Simulation-Based Recursions Raghu Pasupathy (pasupathpurdue.edu) Peter Glynn (glynnstanford.edu) Soumyadip Ghosh (sdghoshgmail.com) Fatemeh Hashemi (fatemeh.s.hashemigmail.com) Abstract: We consider the context of simulation-based recursions," that is, recursions that involve quantities needing to be estimated using a stochastic simulation. Examples include stochastic adaptations of fixed-point and gradient descent recursions obtained by replacing function and derivative values appearing within the recursion by their Monte Carlo counterparts. The primary motivating settings are Simulation Optimization and Stochastic Root Finding problems, where the low-point and the zero of a function are sought, respectively, with only Monte Carlo estimates of the functions appearing within the problem. We ask how much Monte Carlo sampling needs to be performed within simulation-based recursions in order that the resulting iterates remain consistent, and more importantly, \emph{efficient}, where efficient" implies convergence at the fastest possible rate. Answering these questions involves trading-off two types of error inherent in the iterates: the deterministic error due to recursion and the stochastic" error due to sampling. As we demonstrate through a characterization of the relationship between sample sizing and convergence rates, efficiency and consistency are intimately coupled with the speed of the underlying recursion, with faster recursions yielding a wider regime of optimal" sampling rates. The implications of our results to practical implementation are immediate since they provide specific guidance on optimal simulation expenditure within a variety of stochastic recursions. Keywords: sampling, stochastic recursions, stochastic gradient, stochastic line search, simulation optimization Category 1: Other Topics (Optimization of Simulated Systems ) Category 2: Stochastic Programming Category 3: Nonlinear Optimization Citation: Download: [PDF]Entry Submitted: 03/09/2016Entry Accepted: 03/09/2016Entry Last Modified: 05/30/2017Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.