A quantitative theorem supplies uniform rates of convergence for stochastic quasi-Fejér monotone sequences in metric spaces by extending a deterministic regularity notion to the stochastic setting and applying it to proximal-point, Krasnoselskii-Mann, and Busemann subgradient methods.
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Convergence guarantees for stochastic algorithms solving non-unique problems in metric spaces
A quantitative theorem supplies uniform rates of convergence for stochastic quasi-Fejér monotone sequences in metric spaces by extending a deterministic regularity notion to the stochastic setting and applying it to proximal-point, Krasnoselskii-Mann, and Busemann subgradient methods.