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JOURNALS // Avtomatika i Telemekhanika // Archive

Avtomat. i Telemekh., 2016 Issue 8, Pages 105–124 (Mi at14529)

This article is cited 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.

Presented by the member of Editorial Board: P. S. Shcherbakov

Received: 29.01.2015


 English version:
Automation and Remote Control, 2016, 77:8, 1403–1418

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