Objective Improvement in Information-Geometric Optimization

28/08/2013
Auteurs : Yann Ollivier
OAI : oai:www.see.asso.fr:2552:4858
DOI :

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Objective Improvement in Information-Geometric Optimization

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Objective Improvement in Information-Geometric Optimization Youhei Akimoto Project TAO – INRIA Saclay LRI, Bât. 490, Univ. Paris-Sud 91405 Orsay, France Youhei.Akimoto@lri.fr Yann Ollivier CNRS & Univ. Paris-Sud LRI, Bât. 490 91405 Orsay, France yann.ollivier@lri.fr ABSTRACT Information-Geometric Optimization (IGO) is a unified frame- work of stochastic algorithms for optimization problems. Given a family of probability distributions, IGO turns the original optimization problem into a new maximization prob- lem on the parameter space of the probability distributions. IGO updates the parameter of the probability distribution along the natural gradient, taken with respect to the Fisher metric on the parameter manifold, aiming at maximizing an adaptive transform of the objective function. IGO re- covers several known algorithms as particular instances: for the family of Bernoulli distributions IGO recovers PBIL, for the family of Gaussian distributions the pure rank-