Search Results for author: Luyao Guo

Found 5 papers, 0 papers with code

A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization

no code implementations19 Dec 2023 Luyao Guo, Luqing Wang, Xinli Shi, Jinde Cao

In this paper, we propose a communication-efficient method MG-Skip with probabilistic local updates and multi-gossip communications for decentralized composite (smooth + nonsmooth) optimization, whose stepsize is independent of the number of local updates and the network topology.

Distributed Optimization

Revisiting Decentralized ProxSkip: Achieving Linear Speedup

no code implementations12 Oct 2023 Luyao Guo, Sulaiman A. Alghunaim, Kun Yuan, Laurent Condat, Jinde Cao

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.

Distributed Optimization Federated Learning

Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization

no code implementations7 Feb 2023 Luyao Guo, Xinli Shi, Jinde Cao, ZiHao Wang

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

no code implementations6 Dec 2022 Luyao Guo, Jinde Cao, Xinli Shi, Shaofu Yang

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.

Distributed Optimization

DISA: A Dual Inexact Splitting Algorithm for Distributed Convex Composite Optimization

no code implementations5 Sep 2022 Luyao Guo, Xinli Shi, Shaofu Yang, Jinde Cao

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.

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