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Found 4 papers, 0 papers with code
A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization
While the convergence behavior and advantageous acceleration effects of random reshuffling methods are fairly well understood in the smooth setting, much less seems to be known in the nonsmooth case and only few proximal-type random reshuffling approaches with provable guarantees exist.
Convergence of a Normal Map-based Prox-SGD Method under the KL Inequality
In this paper, we present a novel stochastic normal map-based algorithm ($\mathsf{norM}\text{-}\mathsf{SGD}$) for nonconvex composite-type optimization problems and discuss its convergence properties.
Distributed Random Reshuffling over Networks
We show that D-RR inherits favorable characteristics of RR for both smooth strongly convex and smooth nonconvex objective functions.
Convergence of Random Reshuffling Under The Kurdyka-Łojasiewicz Inequality
We conduct a novel convergence analysis for the non-descent RR method with diminishing step sizes based on the KL inequality, which generalizes the standard KL framework.
