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math.OC2023
A New Random Reshuffling Method for Nonsmooth Nonconvex Finite-sum Optimization
Junwen Qiu, Xiao Li, Andre Milzarek
Random reshuffling techniques are prevalent in large-scale applications, such as training neural networks. While the convergence and acceleration effects of random reshuffling-type…
math.OC2023
High Probability Guarantees for Random Reshuffling
Hengxu Yu, Xiao Li
We consider the stochastic gradient method with random reshuffling () for tackling smooth nonconvex optimization problems. finds broad applications in pr…
math.OC2023
Distributed Stochastic Optimization under a General Variance Condition
Kun Huang, Xiao Li, Shi Pu
Distributed stochastic optimization has drawn great attention recently due to its effectiveness in solving large-scale machine learning problems. Though numerous algorithms have be…