A unified recursion framework for stochastic variance-reduced estimation yields high-probability bounds and the first Õ(ε^{-3}) oracle complexity for stochastic optimization with expectation constraints.
A single-loop spider-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization
4 Pith papers cite this work. Polarity classification is still indexing.
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PALM achieves Õ(ε^{-1}) first-order complexity for ε-KKT points in convex-strongly-concave minimax problems with functional constraints and Õ(ε^{-3/2}) for the dual in the convex-concave case.
MoSSP is a new single-loop stochastic penalty method with Polyak or recursive momentum that achieves O(ε^{-4}) or O(ε^{-3}) oracle complexity for stochastic ε-KKT points in nonconvex constrained DC-regularized problems.
Develops a smoothing extension of ESQM for DC optimization over convex composite inequality constraints, proving O(ε^{-3}) iteration complexity to (ε,ε)-KKT points plus convergence in the convex case.
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First-Order Methods for Solving Convex (Strongly) Concave Minimax Problems with Functional Constraints
PALM achieves Õ(ε^{-1}) first-order complexity for ε-KKT points in convex-strongly-concave minimax problems with functional constraints and Õ(ε^{-3/2}) for the dual in the convex-concave case.
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MoSSP: A Momentum-Based Single-Loop Stochastic Penalty Method for Nonconvex Constrained DC-Regularized Optimization
MoSSP is a new single-loop stochastic penalty method with Polyak or recursive momentum that achieves O(ε^{-4}) or O(ε^{-3}) oracle complexity for stochastic ε-KKT points in nonconvex constrained DC-regularized problems.
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A smoothing extended sequential quadratic method for difference-of-convex optimization over a convex composite inequality constraint
Develops a smoothing extension of ESQM for DC optimization over convex composite inequality constraints, proving O(ε^{-3}) iteration complexity to (ε,ε)-KKT points plus convergence in the convex case.