Robust optimization via randomized algorithms

This paper gives an overview on probabilistic approach to robust optimization and chance constrained optimization. The problems are to minimize a linear objective function subject to a parameter dependent convex constraint, where a probability measure is introduced onto the parameter set. Two random...

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Bibliographic Details
Published in2009 ICCAS-SICE : 18-21 August 2009 pp. 1226 - 1229
Main Authors Fujisaki, Y., Wada, T.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
chance constrained optimization
Computer science
Constraint optimization
Control systems
Design optimization
Distribution functions
Mathematical programming
random sampling
Randomized algorithm
robust optimization
Robustness
Sampling methods
Systems engineering and theory
Uncertainty
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Summary:This paper gives an overview on probabilistic approach to robust optimization and chance constrained optimization. The problems are to minimize a linear objective function subject to a parameter dependent convex constraint, where a probability measure is introduced onto the parameter set. Two randomized techniques, the scenario optimization and the sequential optimization, are summarized, where characteristics and advantages of both techniques are discussed.
ISBN:9784907764340
4907764340