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Information-based complexity of black-box convex optimization: a new look via feedback information theory

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Information-based complexity of black-box convex optimization: a new look via feedback information theory

In this talk, I will revisit information complexity of black-box convex optimization, first studied in the seminal work of Nemirovski and Yudin, from the perspective of feedback information theory. These days, large-scale convex programming arises in a variety of applications, and it is important to refine our understanding of its fundamental limitations. The goal of black-box convex optimization is to minimize an unknown convex objective function from a given class over a compact, convex domain using an iterative scheme that generates approximate solutions by querying an oracle for local information about the function being optimized. The information complexity of a given problem class is defined as the smallest number of queries needed to minimize every function in the class to some desired accuracy. We present a simple information-theoretic approach that not only recovers many of the results of Nemirovski and Yudin, but also gives some new bounds pertaining to optimal rates at which iterative convex optimization schemes approach the solution. As a bonus, we give a particularly simple derivation of the minimax lower bound for a certain active learning problem on the unit interval.

This is a joint work with Alexander Rakhlin (UPenn).

- SMILE in Paris

Détails :

Orateur / Oratrice : Maxim Raginsky
Date : 17 mai 2010
Horaire : 14h30 - 14h30
Lieu : IHP Salle 314