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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Vydáno v:	Computational optimization and applications Ročník 78; číslo 3; s. 705 - 740
Hlavní autoři:	Geiersbach, Caroline, Scarinci, Teresa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY Springer US 01.04.2021 Springer Nature B.V
Témata:	Algorithms Approximation Constraints Continuity (mathematics) Convergence Convex and Discrete Geometry Differential inclusions Hilbert space Management Science Mathematical programming methods Mathematics Mathematics and Statistics Nonsmooth and nonconvex optimization Operations Research Operations Research/Decision Theory Optimal control problems involving partial differential equations Optimization Partial differential equations Partial differential equations with randomness Statistics Stochastic programming Differential inclusions Optimal control problems involving partial differential equations Partial differential equations with randomness Nonsmooth and nonconvex optimization Mathematical programming methods Stochastic programming
ISSN:	1573-2894, 0926-6003, 1573-2894
On-line přístup:	Získat plný text
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