Lifts CCCP to Wasserstein space for DC functionals on measures, proves almost stationarity under smoothness/strong-convexity assumptions, and applies to MMD/ED with local convergence and faster empirical runs.
Stochastic Difference-of-Convex Optimization with Mo- mentum.arXiv preprint arXiv:2510.17503, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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Difference of Convex Programming in the Wasserstein Space with Applications to MMD Optimization
Lifts CCCP to Wasserstein space for DC functionals on measures, proves almost stationarity under smoothness/strong-convexity assumptions, and applies to MMD/ED with local convergence and faster empirical runs.
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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.