Dual Smoothing for Decentralized Optimization

Fuente: arXiv
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Main Authors: Rogozin, Alexander, Nguyen, Nhat Trung, Zenuzagh, Hamed Azami, Gasnikov, Alexander
Format: Preprint
Published: 2025
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author Rogozin, Alexander
Nguyen, Nhat Trung
Zenuzagh, Hamed Azami
Gasnikov, Alexander
author_facet Rogozin, Alexander
Nguyen, Nhat Trung
Zenuzagh, Hamed Azami
Gasnikov, Alexander
contents Decentralized optimization is widely used in different fields of study such as distributed learning, signal processing, and various distributed control problems. In these types of problems, nodes of the network are connected to each other and seek to optimize some objective function. In this article, we present a method for smoothing the non-smooth and non-strongly convex problems. This is done using the dual smoothing technique. We study two types of problems: consensus optimization of linear models and coupled constraints optimization. It is shown that these two problem classes are dual to each other.
format Preprint
id arxiv_https___arxiv_org_abs_2512_08167
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Dual Smoothing for Decentralized Optimization
Rogozin, Alexander
Nguyen, Nhat Trung
Zenuzagh, Hamed Azami
Gasnikov, Alexander
Optimization and Control
Decentralized optimization is widely used in different fields of study such as distributed learning, signal processing, and various distributed control problems. In these types of problems, nodes of the network are connected to each other and seek to optimize some objective function. In this article, we present a method for smoothing the non-smooth and non-strongly convex problems. This is done using the dual smoothing technique. We study two types of problems: consensus optimization of linear models and coupled constraints optimization. It is shown that these two problem classes are dual to each other.
title Dual Smoothing for Decentralized Optimization
topic Optimization and Control
url https://arxiv.org/abs/2512.08167