Black-box optimization with a politician

Sébastien Bubeck; Yin-Tat Lee

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arXiv · 1602.04847

Black-box optimization with a politician

Sébastien Bubeck·Yin-Tat Lee

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Abstract

We propose a new framework for black-box convex optimization which is well-suited for situations where gradient computations are expensive. We derive a new method for this framework which leverages several concepts from convex optimization, from standard first-order methods (e.g. gradient descent or quasi-Newton methods) to analytical centers (i.e. minimizers of self-concordant barriers). We demonstrate empirically that our new technique compares favorably with state of the art algorithms (such as BFGS).

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Sébastien Bubeck, Yin-Tat Lee. 2016-02-15. Black-box optimization with a politician. https://arxiv.org/abs/1602.04847

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