New first-order methods compute surely feasible approximate solutions to constrained stochastic convex problems with optimal or near-optimal oracle complexity.
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First-order methods for stochastic and finite-sum convex optimization with deterministic constraints
New first-order methods compute surely feasible approximate solutions to constrained stochastic convex problems with optimal or near-optimal oracle complexity.