Please use this identifier to cite or link to this item: https://doi.org/10.1145/1921598.1921603
Title: The stochastic root-finding problem: Overview, solutions, and open questions
Authors: Pasupathy, R.
Kim, S. 
Keywords: Sample-average approximation
Stochastic approximation
Stochastic root finding
Issue Date: Mar-2011
Source: Pasupathy, R., Kim, S. (2011-03). The stochastic root-finding problem: Overview, solutions, and open questions. ACM Transactions on Modeling and Computer Simulation 21 (3) : -. ScholarBank@NUS Repository. https://doi.org/10.1145/1921598.1921603
Abstract: The stochastic root-finding problem (SRFP) is that of finding the zero(s) of a vector function, that is, solving a nonlinear system of equations when the function is expressed implicitly through a stochastic simulation. SRFPs are equivalently expressed as stochastic fixed-point problems, where the underlying function is expressed implicitly via a noisy simulation. After motivating SRFPs using a few examples, we review available methods to solve such problems on constrained Euclidean spaces. We present the current literature as three broad categories, and detail the basic theoretical results that are currently known in each of the categories. With a view towards helping the practitioner, we discuss speciic variations in their implementable form, and provide references to computer code when easily available. Finally, we list a few questions that are worthwhile research pursuits from the standpoint of advancing our knowledge of the theoretical underpinnings and the implementation aspects of solutions to SRFPs. © 2011 ACM.
Source Title: ACM Transactions on Modeling and Computer Simulation
URI: http://scholarbank.nus.edu.sg/handle/10635/63373
ISSN: 10493301
DOI: 10.1145/1921598.1921603
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