Randomized gradient-free methods in convex optimization

Alexander Gasnikov; Darina Dvinskikh; Pavel Dvurechensky; Eduard Gorbunov; Aleksander Beznosikov; Aleksandr Lobanov

doi:10.1007/978-3-030-54621-2_859-1

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arXiv · 2211.13566

Randomized gradient-free methods in convex optimization

Alexander Gasnikov·Darina Dvinskikh·Pavel Dvurechensky·Eduard Gorbunov·Aleksander Beznosikov·Aleksandr Lobanov

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Abstract

This review presents modern gradient-free methods to solve convex optimization problems. By gradient-free methods, we mean those that use only (noisy) realizations of the objective value. We are motivated by various applications where gradient information is prohibitively expensive or even unavailable. We mainly focus on three criteria: oracle complexity, iteration complexity, and the maximum permissible noise level.

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BibTeX RIS

Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky, Eduard Gorbunov, Aleksander Beznosikov, Aleksandr Lobanov. 2022-11-24. Randomized gradient-free methods in convex optimization. https://doi.org/10.1007/978-3-030-54621-2_859-1

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