Optimization with hidden constraints and embedded Monte Carlo computations

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43954

Title:	Optimization with hidden constraints and embedded Monte Carlo computations
Authors:	Chen, X Kelley, CT
Issue Date:	Mar-2016
Source:	Optimization and engineering, Mar. 2016, v. 17, no. 1, p. 157-175
Abstract:	In this paper we explore the convergence properties of deterministic direct search methods when the objective function contains a stochastic or Monte Carlo simulation. We present new results for the case where the objective is only defined on a set with certain minimal regularity properties. We present two numerical examples to illustrate the ideas.
Keywords:	Hidden constraints Monte carlo simulation Sampling methods Water resource policy
Publisher:	Springer
Journal:	Optimization and engineering
ISSN:	1389-4420
EISSN:	1573-2924
DOI:	10.1007/s11081-015-9302-1
Rights:	© Springer Science+Business Media New York 2015 This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11081-015-9302-1
Appears in Collections:	Journal/Magazine Article

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Status	open access
File Version	Final Accepted Manuscript

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