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Avtomatika i Telemekhanika, 2016, Issue 8, Pages 105–124
(Mi at14529)
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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
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.
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
Linking options:
https://www.mathnet.ru/eng/at14529 https://www.mathnet.ru/eng/at/y2016/i8/p105
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Statistics & downloads: |
Abstract page: | 302 | Full-text PDF : | 74 | References: | 44 | First page: | 21 |
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