# 38515

## Автор(ов):

2

Параметры публикации

## Тип публикации:

Статья в журнале/сборнике

## Название:

Saddle point mirror descent algorithm for the robust PageRank problem

0005-1179

## DOI:

doi:10.1134/S0005117916080075

## Наименование источника:

• Automation and Remote Control

Vol. 77, No. 8

• New York

2016

## Страницы:

1403-1418
Аннотация
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.

## Библиографическая ссылка:

Назин А.В., Тремба А.А. Saddle point mirror descent algorithm for the robust PageRank problem // Automation and Remote Control. 2016. Vol. 77, No. 8. С. 1403-1418.

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