NDLI: Sorting noisy data with partial information

Content Provider	ACM Digital Library
Author	Vijayaraghavan, Aravindan Makarychev, Konstantin Makarychev, Yury
Abstract	In this paper, we propose two semi-random models for the Minimum Feedback Arc Set Problem and present approximation algorithms for them. In the first model, which we call the Random Edge Flipping model, an instance is generated as follows. We start with an arbitrary acyclic directed graph and then randomly flip its edges (the adversary may later un-flip some of them). In the second model, which we call the Random Backward Edge model, again we start with an arbitrary acyclic graph but now add new random backward edges (the adversary may delete some of them). For the first model, we give an approximation algorithm that finds a solution of cost (1+ δ) OPT + n polylog n, where OPT is the cost of the optimal solution. For the second model, we give an approximation algorithm that finds a solution of cost O(planted) + n polylog n, where planted is the cost of the planted solution. Additionally, we present an approximation algorithm for semi-random instances of Minimum Directed Balanced Cut.
Starting Page	515
Ending Page	528
Page Count	14
File Format	PDF
ISBN	9781450318594
DOI	10.1145/2422436.2422492
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-01-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Minimum feedback arc set Average-case analysis Semi-random model Approximation algorithm
Content Type	Text
Resource Type	Article

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Sorting noisy data with partial information