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On the approximate domain optimization of deterministic and expected value criteria

Author(s):

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

IEEE Conference on Decision and Control, pp. 4933-4938, Cancun, Mexico
Abstract:

We define the concept of approximate domain optimizer for deterministic and expected value optimization criteria. 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 than 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.

Year:

2008
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { VisLyg:2008:IFA_3239,
    author={A. Lecchini Visintini and J. Lygeros and Jan M. Maciejowski},
    title={{On the approximate domain optimization of deterministic and
	  expected value criteria}},
    booktitle={IEEE Conference on Decision and Control},
    pages={4933--4938},
    year={2008},
    address={},
    month=dec,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3239}
}
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