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2008
IEEE

On the approximate domain optimization of deterministic and expected value criteria

10 years 6 months ago
On the approximate domain optimization of deterministic and expected value criteria
— 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.
Andrea Lecchini-Visintini, John Lygeros, Jan M. Ma
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where CDC
Authors Andrea Lecchini-Visintini, John Lygeros, Jan M. Maciejowski
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