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.
Tight Analysis of Difference-of- Convex Algorithm (DCA) Improves Convergence Rates for Proximal Gradient Descent.arXiv preprint arXiv:2503.04486, 2025
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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.