A new step size rule lets boosted stochastic Frank-Wolfe match ordinary stochastic Frank-Wolfe rates on nonconvex and quasar-convex problems and deliver faster empirical convergence on sparse logistic regression and quantum tomography.
arXiv preprint arXiv:2510.11440 (2025)
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Proposes (L0, L1)-Frank-Wolfe and adaptive variant claiming superior convergence rates for (L0, L1)-smooth objectives over classical Frank-Wolfe.
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Boosted Stochastic Frank-Wolfe for Constrained Nonconvex Optimization
A new step size rule lets boosted stochastic Frank-Wolfe match ordinary stochastic Frank-Wolfe rates on nonconvex and quasar-convex problems and deliver faster empirical convergence on sparse logistic regression and quantum tomography.
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Frank-Wolfe Algorithms for (L0, L1)-smooth functions
Proposes (L0, L1)-Frank-Wolfe and adaptive variant claiming superior convergence rates for (L0, L1)-smooth objectives over classical Frank-Wolfe.