PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimistic optimization of deterministic functions without the knowledge of its smoothness
Rémi Munos
NIPS 2011 2011.

Abstract

We consider a global optimization problem of a deterministic function f in a semi-metric space, given a finite budget of n evaluations. The function f is assumed to be locally smooth (around one of its global maxima) with respect to a semi-metric. We describe two algorithms based on optimistic exploration that use a hierarchical partitioning of the space at all scales. A first contribution is an algorithm, DOO, that requires the knowledge of . We report a finite-sample performance bound in terms of a measure of the quantity of near-optimal states. We then define a second algorithm, SOO, which does not require the knowledge of the semi-metric under which f is smooth, and whose performance is almost as good as DOO optimally-fitted.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:COMPLACS
ID Code:8984
Deposited By:Rémi Munos
Deposited On:21 February 2012