Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization
–Neural Information Processing Systems
This paper studies delayed stochastic algorithms for weakly convex optimization in a distributed network with workers connected to a master node. Recently, Xu et al. 2022 showed that an inertial stochastic subgradient method converges at a rate of O(τ
Neural Information Processing Systems
Feb-8-2025, 09:59:14 GMT
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