The power of first-order smooth optimization for black-box non-smooth problems

Alexander Gasnikov · Anton Novitskii · Vasilii Novitskii · Farshed Abdukhakimov · Dmitry Kamzolov · Aleksandr Beznosikov · Martin Takac · Pavel Dvurechenskii · Bin Gu

Hall E #731

Keywords: [ OPT: Convex ] [ OPT: Global Optimization ] [ OPT: Stochastic ] [ T: Optimization ] [ OPT: Zero-order and Black-box Optimization ]

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Wed 20 Jul 3:30 p.m. PDT — 5:30 p.m. PDT
Spotlight presentation: Deep Learning/Optimization
Wed 20 Jul 1:30 p.m. PDT — 3 p.m. PDT


Gradient-free/zeroth-order methods for black-box convex optimization have been extensively studied in the last decade with the main focus on oracle calls complexity. In this paper, besides the oracle complexity, we focus also on iteration complexity, and propose a generic approach that, based on optimal first-order methods, allows to obtain in a black-box fashion new zeroth-order algorithms for non-smooth convex optimization problems. Our approach not only leads to optimal oracle complexity, but also allows to obtain iteration complexity similar to first-order methods, which, in turn, allows to exploit parallel computations to accelerate the convergence of our algorithms. We also elaborate on extensions for stochastic optimization problems, saddle-point problems, and distributed optimization.

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