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Stochastic Forward–Backward Splitting for Monotone Inclusions
2016
Journal of Optimization Theory and Applications
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-asymptotic error analysis in expectation for the strongly monotone case, as
doi:10.1007/s10957-016-0893-2
fatcat:ix6l5fv3xrhgvnlekp3v7toh7q