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Paper detail

Regret analysis of the Piyavskii-Shubert algorithm for global Lipschitz optimization

We consider the problem of maximizing a non-concave Lipschitz multivariate function over a compact domain by sequentially querying its (possibly perturbed) values. We study a natural algorithm designed originally by Piyavskii and Shubert in 1972, for which we prove new bounds on the number of evaluations of the function needed to reach or certify a given optimization accuracy. Our analysis uses a bandit-optimization viewpoint and solves an open problem from Hansen et al.\ (1991) by bounding the number of evaluations to certify a given accuracy with a near-optimal sum of packing numbers.

preprint2022arXivOpen access
Clément BouttierTommaso CesariMélanie DucoffeSébastien Gerchinovitz
math.OCMachine Learning
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Clément BouttierTommaso CesariMélanie DucoffeSébastien Gerchinovitz

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