Stochastic Forward–Backward Splitting for Monotone Inclusions
We propose and analyze the convergence of a novel stochastic algorithm for monotone inclusions that are sum of a maximal monotone operator and a single-valued cocoercive operator. The algorithm we propose is a natural stochastic extension of the classical forward–backward method. We provide a non-as...
Main Authors: | Villa, Silvia, Vũ, Bang Công, Rosasco, Lorenzo Andrea |
---|---|
Other Authors: | Massachusetts Institute of Technology. Laboratory for Computational and Statistical Learning |
Format: | Article |
Language: | English |
Published: |
Springer US
2016
|
Online Access: | http://hdl.handle.net/1721.1/103419 https://orcid.org/0000-0001-6376-4786 |
Similar Items
Similar Items
-
Almost sure convergence of the forward–backward–forward splitting algorithm
by: Vu, Bang Cong
Published: (2017) -
Convergence of the forward-backward algorithm: beyond the worst-case with the help of geometry
by: Garrigos, Guillaume, et al.
Published: (2023) -
Convergence of Stochastic Proximal Gradient Algorithm
by: Rosasco, Lorenzo, et al.
Published: (2021) -
Wasserstein distance estimates for stochastic integrals by forward-backward stochastic calculus
by: Breton, Jean-Christophe, et al.
Published: (2022) -
General linear forward and backward Stochastic difference equations with applications
by: Xu, Juanjuan, et al.
Published: (2020)