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Found 5 papers, 0 papers with code
MG-Skip: Random Multi-Gossip Skipping Method for Nonsmooth Distributed Optimization
Distributed optimization methods with probabilistic local updates have recently gained attention for their provable ability to communication acceleration.
Revisiting Decentralized ProxSkip: Achieving Linear Speedup
We demonstrate that the leading communication complexity of ProxSkip is $\mathcal{O}\left(\frac{p\sigma^2}{n\epsilon^2}\right)$ for non-convex and convex settings, and $\mathcal{O}\left(\frac{p\sigma^2}{n\epsilon}\right)$ for the strongly convex setting, where $n$ represents the number of nodes, $p$ denotes the probability of communication, $\sigma^2$ signifies the level of stochastic noise, and $\epsilon$ denotes the desired accuracy level.
Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization
The proposed algorithm uses uncoordinated network-independent constant stepsizes and only needs to approximately solve a sequence of proximal mappings, which is advantageous for solving decentralized composite optimization problems where the proximal mappings of the nonsmooth loss functions may not have analytical solutions.
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization
In this paper, we propose a novel primal-dual proximal splitting algorithm (PD-PSA), named BALPA, for the composite optimization problem with equality constraints, where the loss function consists of a smooth term and a nonsmooth term composed with a linear mapping.
DISA: A Dual Inexact Splitting Algorithm for Distributed Convex Composite Optimization
In this paper, we propose a novel Dual Inexact Splitting Algorithm (DISA) for distributed convex composite optimization problems, where the local loss function consists of a smooth term and a possibly nonsmooth term composed with a linear mapping.
