A zeroth order method for stochastic weakly convex optimization

V. Kungurtsev () and F. Rinaldi ()
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V. Kungurtsev: Czech Technical University in Prague
F. Rinaldi: Università di Padova

Computational Optimization and Applications, 2021, vol. 80, issue 3, No 3, 753 pages

Abstract: Abstract In this paper, we consider stochastic weakly convex optimization problems, however without the existence of a stochastic subgradient oracle. We present a derivative free algorithm that uses a two point approximation for computing a gradient estimate of the smoothed function. We prove convergence at a similar rate as state of the art methods, however with a larger constant, and report some numerical results showing the effectiveness of the approach.

Keywords: Derivative free optimization; Zeroth order optimization; Stochastic optimization; Weakly convex functions; 90C56; 90C15; 65K05 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10589-021-00313-3

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