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In this paper, a new theory is developed for first-order stochastic convex optimization, showing that the global convergence rate is sufficiently quantified by a local growth rate of the objective function in a neighborhood of the optimal solutions.
Monotone operators and the proximal point algorithm
R. Tyrrell Rockafellar · 1976
Earlier work this paper cites.
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Arkadii Semenovich. Nemirovsky A.S. and D. B Yudin · 1983
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