norM-DSGT and norM-ED achieve centralized stochastic proximal-gradient rates for distributed composite objectives, with norM-ED transient time O(n^3/(1-λ)^2).
A stochastic proximal gradient framework for decentralized non-convex composite optimiza- tion: Topology-independent sample complexity and communication ef- ficiency
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Treating stochastic and deterministic gradients separately in mini-batch SGD yields faster convergence and smaller error radius than uniform treatment, with further gains under strong convexity.
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Distributed Normal Map-based Stochastic Proximal Gradient Methods over Networks
norM-DSGT and norM-ED achieve centralized stochastic proximal-gradient rates for distributed composite objectives, with norM-ED transient time O(n^3/(1-λ)^2).
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Stochastic versus Deterministic in Stochastic Gradient Descent
Treating stochastic and deterministic gradients separately in mini-batch SGD yields faster convergence and smaller error radius than uniform treatment, with further gains under strong convexity.