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Saddle point mirror descent algorithm for the robust PageRank problem

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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.

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Authors and Affiliations

  1. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia

    A. V. Nazin & A. A. Tremba

  2. National Research University Higher School of Economics, Moscow, Russia

    A. V. Nazin & A. A. Tremba

Authors
  1. A. V. Nazin
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  2. A. A. Tremba
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Correspondence to A. V. Nazin.

Additional information

Original Russian Text © A.V. Nazin, A.A. Tremba, 2016, published in Avtomatika i Telemekhanika, 2016, No. 8, pp. 105–124.

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Nazin, A.V., Tremba, A.A. Saddle point mirror descent algorithm for the robust PageRank problem. Autom Remote Control 77, 1403–1418 (2016). https://doi.org/10.1134/S0005117916080075

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  • DOI: https://doi.org/10.1134/S0005117916080075

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