Stochastic Proximal Gradient Methods for Nonconvex Problems in Hilbert Spaces

For finite-dimensional problems, stochastic approximation methods have long been used to solve stochastic optimization problems. Their application to infinite-dimensional problems is less understood, particularly for nonconvex objectives. This paper presents convergence results for the stochastic pr...

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Vydané v:	arXiv.org
Hlavní autori:	Geiersbach, Caroline, Scarinci, Teresa
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Ithaca Cornell University Library, arXiv.org 13.01.2021
Predmet:	Algorithms Approximation Computer simulation Constraints Continuity (mathematics) Convergence Hilbert space Nonlinear programming Optimization Partial differential equations
ISSN:	2331-8422
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