A Continuous-Time Distributed Algorithm for Solving a Class of Decomposable Nonconvex Quadratic Programming

In this paper, a continuous-time distributed algorithm is presented to solve a class of decomposable quadratic programming problems. In the quadratic programming, even if the objective function is nonconvex, the algorithm can still perform well under an extra condition combining with the objective,...

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Vydané v:Journal of Artificial Intelligence and Soft Computing Research Ročník 8; číslo 4; s. 283 - 291
Hlavní autori: Zhao, Yan, Liu, Qingshan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Warsaw De Gruyter Open 01.10.2018
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Predmet:
Algorithms
consensus
Decomposable nonconvex quadratic programming
Decomposition
Lyapunov method
multi-agent network
Multiagent systems
Optimization
Quadratic programming
ISSN:2083-2567, 2083-2567, 2449-6499
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Shrnutí:In this paper, a continuous-time distributed algorithm is presented to solve a class of decomposable quadratic programming problems. In the quadratic programming, even if the objective function is nonconvex, the algorithm can still perform well under an extra condition combining with the objective, constraint and coupling matrices. Inspired by recent advances in distributed optimization, the proposed continuous-time algorithm described by multi-agent network with consensus is designed and analyzed. In the network, each agent only accesses the local information of its own and from its neighbors, then all the agents in a connected network cooperatively find the optimal solution with consensus.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2083-2567
2083-2567
2449-6499
DOI:10.1515/jaiscr-2018-0018