Optimistic optimization of deterministic functions without the knowledge of its smoothnessReportar como inadecuado




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1 SEQUEL - Sequential Learning LIFL - Laboratoire d-Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d-Automatique, Génie Informatique et Signal

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.





Autor: Rémi Munos -

Fuente: https://hal.archives-ouvertes.fr/



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