Researchers have developed new algorithms for stochastic optimization problems with nonsmooth and nonconvex objectives under convex constraints. These algorithms achieve optimal convergence rates, with projection-based and projection-free methods reaching a $\widetilde O(\delta^{-1}\varepsilon^{-3})$ rate using stochastic gradients and a $\widetilde O(d\delta^{-1}\varepsilon^{-3})$ rate using zeroth-order queries. The findings indicate that constraints and nonsmoothness do not degrade convergence rates in nonconvex stochastic optimization, with the analysis relying on a reduction to online linear optimization. AI
RANK_REASON Academic paper detailing new algorithms and theoretical results in optimization. [lever_c_demoted from research: ic=1 ai=0.4]
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