Saddle point mirror descent algorithm for the robust PageRank problem

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• 作者：A. V. Nazin ; A. A. Tremba
• 刊名：Automation and Remote Control
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：77
• 期：8
• 页码：1403-1418
• 全文大小：389 KB
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Calculus of Variations and Optimal Control
  Systems Theory and Control
  Automation and Robotics
  Mechanical Engineering
  Computer-Aided Engineering and Design
  Russian Library of Science
  
• 出版者：MAIK Nauka/Interperiodica distributed exclusively by Springer Science+Business Media LLC.
• ISSN：1608-3032
• 卷排序：77

文摘

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