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A Randomized Approximation Algorithm for Parameterized 3-D Matching Counting Problem

Authors: Yunlong Liu 0001; Jianer Chen; Jianxin Wang 0001;

A Randomized Approximation Algorithm for Parameterized 3-D Matching Counting Problem

Abstract

The computational complexity of counting the number of matchings of size k in a given triple set remains open, and it is conjectured that the problem is infeasible. In this paper, we present a fixed parameter tractable randomized approximation scheme (FPTRAS) for the problem. More precisely, we develop a randomized algorithm that, on given positive real numbers e and δ, and a given set S of n triples and an integer k, produces a number h in time O(5.483kn2 ln(2/δ)/e2) such that prob[(1 - e)h0 ≤ h ≤ (1 + e)h0] > 1 - δ where h0 is the total number of matchings of size k in the triple set S. Our algorithm is based on the recent improved color-coding techniques and the Monte-Carlo self-adjusting coverage algorithm developed by Karp and Luby.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
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