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Avtomatika i Telemekhanika, 2016, Issue 8, Pages 105–124 (Mi at14529)  
This article is cited in 2 scientific papers (total in 2 papers)

Stochastic Systems, Queuing Systems

Saddle point mirror descent algorithm for the robust PageRank problem

A. V. Nazin, A. A. Tremba

Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia
Full-text PDF (955 kB) Citations (2)
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Abstract: In order to solve robust PageRank problem a saddle-point Mirror Descent algorithm for solving convex-concave optimization problems is enhanced and studied. The algorithm is based on two proxy functions, which use specificities of value sets to be optimized on (min-max search). In robust PageRank case the ones are entropy-like function and square of Euclidean norm. The saddle-point Mirror Descent algorithm application to robust PageRank leads to concrete complexity results, which are being discussed alongside with illustrative numerical example.
Funding agency Grant number
Russian Foundation for Basic Research 14-07-00067
13-01-00406
Ministry of Education and Science of the Russian Federation 11.G34.31.0073
This work was supported in part by the Russian Foundation for Basic Research, projects nos. 14-07-00067 and 13-01-00406. First author is also partially supported by MIPT Premolab Laboratory, Russian Federation grant no. 11.G34.31.0073.
Presented by the member of Editorial Board: P. S. Shcherbakov

Received: 29.01.2015
English version:
Automation and Remote Control, 2016, Volume 77, Issue 8, Pages 1403–1418
DOI: https://doi.org/10.1134/S0005117916080075
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: A. V. Nazin, A. A. Tremba, “Saddle point mirror descent algorithm for the robust PageRank problem”, Avtomat. i Telemekh., 2016, no. 8, 105–124; Autom. Remote Control, 77:8 (2016), 1403–1418
Citation in format AMSBIB
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\pages 105--124
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    • This publication is cited in the following 2 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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