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dc.contributor.author
Marelli, Damian Edgardo  
dc.contributor.author
Xu, Yong  
dc.contributor.author
Fu, Minyue  
dc.contributor.author
Huang, Zenghong  
dc.date.available
2022-12-30T01:31:14Z  
dc.date.issued
2021-10  
dc.identifier.citation
Marelli, Damian Edgardo; Xu, Yong; Fu, Minyue; Huang, Zenghong; Distributed Newton Optimization With Maximized Convergence Rate; Institute of Electrical and Electronics Engineers; IEEE Transactions on Automatic Control; 67; 10; 10-2021; 5555-5562  
dc.identifier.issn
0018-9286  
dc.identifier.uri
http://hdl.handle.net/11336/182877  
dc.description.abstract
The distributed optimization problem is set up in a collection of nodes interconnected via a communication network. The goal is to find the minimizer of a global objective function formed by the sum of local functions known at individual nodes. A number of methods, having different advantages, are available for addressing this problem. The goal of this article is to achieve the maximum possible convergence rate. As the first step toward this end, we propose a new method, which we show converges faster than other available options. As the second step toward our goal, we complement the proposed method with a fully distributed method for estimating the optimal step size that maximizes the convergence rate. We provide theoretical guarantees for the convergence of the resulting method in a neighborhood of the solution. We present numerical experiments showing that, when using the same step size, our method converges significantly faster than its rivals. Experiments also show that the distributed step-size estimation method achieves an asymptotic convergence rate very close to the theoretical maximum.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Institute of Electrical and Electronics Engineers  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
CONVERGENCE  
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LINEAR PROGRAMMING  
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OPTIMIZATION METHODS  
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DISTRIBUTED ALGORITHMS  
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MINIMIZATION  
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ESTIMATION  
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COMMUNICATION NETWORKS  
dc.subject.classification
Matemática Aplicada  
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Matemáticas  
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CIENCIAS NATURALES Y EXACTAS  
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Ciencias de la Computación  
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Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Distributed Newton Optimization With Maximized Convergence Rate  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2022-08-31T14:58:26Z  
dc.journal.volume
67  
dc.journal.number
10  
dc.journal.pagination
5555-5562  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
New York  
dc.description.fil
Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina  
dc.description.fil
Fil: Xu, Yong. Guangdong University Of Technology; China  
dc.description.fil
Fil: Fu, Minyue. Universidad de Newcastle; Australia  
dc.description.fil
Fil: Huang, Zenghong. Guangdong University Of Technology; China  
dc.journal.title
IEEE Transactions on Automatic Control  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/9591351  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TAC.2021.3123244