Year: 2021
Type: article
Abstract: We deal with a stochastic programming problem that can be inconsistent.To overcome the inconsistency we apply Tikhonov's regularization technique, and, using recent results on the convergence rate of empirical measures in Wasserstein metric, we treat the following two related problems:1.A choice of regularization parameters that guarantees the convergence of the minimization procedure.2. Estimation of the rate of convergence in probability.Considering both light and heavy tail distributions and Lipschitz objective functions (which can be unbounded), we obtain the power bounds for the convergence rate.
Source: Kybernetika
Authors: Evgueni Gordienko, Yury Gryazin
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W3133506487