Simulation-Based Optimality Tests for Stochastic Programs

Güzin Bayraksan (), David P. Morton () and Amit Partani ()
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Güzin Bayraksan: University of Arizona
David P. Morton: The University of Texas at Austin
Amit Partani: The University of Texas at Austin

Chapter Chapter 3 in Stochastic Programming, 2010, pp 37-55 from Springer

Abstract: Abstract Assessing whether a solution is optimal, or near-optimal, is fundamental in optimization. We describe a simple simulation-based procedure for assessing the quality of a candidate solution to a stochastic program. The procedure is easy to implement, widely applicable, and yields point and interval estimators on the candidate solutions optimality gap. Our simplest procedure allows for significant computational improvements. The improvements we detail aim to reduce computational effort through single- and two-replication procedures, reduce bias via a class of generalized jackknife estimators, and reduce variance by using a randomized quasi-Monte Carlo scheme.

Keywords: Monte Carlo; Candidate Solution; Stochastic Program; Latin Hypercube Sampling; Interval Estimator (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4419-1642-6_3

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DOI: 10.1007/978-1-4419-1642-6_3

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