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Black-box Optimization with a Politician

2020-03-06 | |  82 |   44 |   0

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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