Optimization with hidden constraints and embedded Monte Carlo computations

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 pres...

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Bibliographic Details
Published in:Optimization and engineering 2016-03, Vol.17 (1), p.157-175
Main Authors: Chen, Xiaojun, Kelley, C. T.
Format: Article
Language:English
Subjects:
Computer simulation
Control
Convergence
Engineering
Environmental Management
Financial Engineering
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Monte Carlo methods
Monte Carlo simulation
Operations Research/Decision Theory
Optimization
Regularity
Search methods
Systems Theory
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Summary: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.
ISSN:1389-4420
1573-2924
DOI:10.1007/s11081-015-9302-1