A unified framework for decentralized stochastic subgradient methods with compressed communication is proposed, proving global convergence for nonsmooth nonconvex objectives via differential inclusions and developing new variants with numerical support.
Decentralized composite optimiza- tion with compression.arXiv preprint arXiv:2108.04448, 2021
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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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Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization
A unified framework for decentralized stochastic subgradient methods with compressed communication is proposed, proving global convergence for nonsmooth nonconvex objectives via differential inclusions and developing new variants with numerical support.
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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).