Asynchronous Gossip-Based Gradient-Free Method for Multiagent Optimization

This paper considers the constrained multiagent optimization problem. The objective function of the problem is a sum of convex functions, each of which is known by a specific agent only. For solving this problem, we propose an asynchronous distributed method that is based on gradient-free oracles...

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Main Author: Deming Yuan
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2014/618641
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author Deming Yuan
author_facet Deming Yuan
author_sort Deming Yuan
collection DOAJ
description This paper considers the constrained multiagent optimization problem. The objective function of the problem is a sum of convex functions, each of which is known by a specific agent only. For solving this problem, we propose an asynchronous distributed method that is based on gradient-free oracles and gossip algorithm. In contrast to the existing work, we do not require that agents be capable of computing the subgradients of their objective functions and coordinating their step size values as well. We prove that with probability 1 the iterates of all agents converge to the same optimal point of the problem, for a diminishing step size.
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institution Kabale University
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spelling doaj-art-f02e8f6c122d4515afe3131f19d440692025-02-03T01:23:05ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/618641618641Asynchronous Gossip-Based Gradient-Free Method for Multiagent OptimizationDeming Yuan0College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210046, ChinaThis paper considers the constrained multiagent optimization problem. The objective function of the problem is a sum of convex functions, each of which is known by a specific agent only. For solving this problem, we propose an asynchronous distributed method that is based on gradient-free oracles and gossip algorithm. In contrast to the existing work, we do not require that agents be capable of computing the subgradients of their objective functions and coordinating their step size values as well. We prove that with probability 1 the iterates of all agents converge to the same optimal point of the problem, for a diminishing step size.http://dx.doi.org/10.1155/2014/618641
spellingShingle Deming Yuan
Asynchronous Gossip-Based Gradient-Free Method for Multiagent Optimization
Abstract and Applied Analysis
title Asynchronous Gossip-Based Gradient-Free Method for Multiagent Optimization
title_full Asynchronous Gossip-Based Gradient-Free Method for Multiagent Optimization
title_fullStr Asynchronous Gossip-Based Gradient-Free Method for Multiagent Optimization
title_full_unstemmed Asynchronous Gossip-Based Gradient-Free Method for Multiagent Optimization
title_short Asynchronous Gossip-Based Gradient-Free Method for Multiagent Optimization
title_sort asynchronous gossip based gradient free method for multiagent optimization
url http://dx.doi.org/10.1155/2014/618641
work_keys_str_mv AT demingyuan asynchronousgossipbasedgradientfreemethodformultiagentoptimization