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Approximate domain optimization for deterministic and expected value criteria

Author(s):

A. Lecchini Visintini, J. Lygeros, Jan M. Maciejowski
Conference/Journal:

vol. AUT08-03
Abstract:

We define the concept of approximate domain optimizer for deterministic and expected value optimization criteria. Very roughly speaking, a candidate optimizer is an approximate domain optimizer if only a small fraction of the optimization domain is more than a little better from it. We show how this concept relates to commonly used approximate optimizer notions for the case of Lipschitz criteria. We then show how random extractions from an appropriate probability distribution can generate approximate domain optimizers with high confidence. Finally, we discuss how such random extractions can be performed using Markov Chain Monte Carlo methods and provide preliminary bounds on the complexity of the resulting algorithms.

Year:

2008
Type of Publication:

(04)Technical Report
Supervisor:



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% Autogenerated BibTeX entry
@TechReport { VisLyg:2008:IFA_3048,
    author={A. Lecchini Visintini and J. Lygeros and Jan M. Maciejowski},
    title={{Approximate domain optimization for deterministic and
	  expected value criteria}},
    institution={},
    year={2008},
    number={},
    address={},
    month=mar,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3048}
}
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