Exploiting Reduction Rules and Data Structures: Local Search for Minimum Vertex Cover in Massive Graphs

09/19/2015
by   Yi Fan, et al.
0

The Minimum Vertex Cover (MinVC) problem is a well-known NP-hard problem. Recently there has been great interest in solving this problem on real-world massive graphs. For such graphs, local search is a promising approach to finding optimal or near-optimal solutions. In this paper we propose a local search algorithm that exploits reduction rules and data structures to solve the MinVC problem in such graphs. Experimental results on a wide range of real-word massive graphs show that our algorithm finds better covers than state-of-the-art local search algorithms for MinVC. Also we present interesting results about the complexities of some well-known heuristics.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/08/2019

Local Search for Group Closeness Maximization on Big Graphs

In network analysis and graph mining, closeness centrality is a popular ...
research
03/23/2016

Cosolver2B: An Efficient Local Search Heuristic for the Travelling Thief Problem

Real-world problems are very difficult to optimize. However, many resear...
research
02/04/2014

NuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover

The Minimum Vertex Cover (MVC) problem is a prominent NP-hard combinator...
research
07/05/2019

Improved local search for graph edit distance

Graph Edit Distance (GED) measures the dissimilarity between two graphs ...
research
09/02/2020

An enhanced simulation-based iterated local search metaheuristic for gravity fed water distribution network design optimization

The gravity fed water distribution network design (WDND) optimization pr...
research
01/14/2021

Time-critical testing and search problems

This paper introduces a problem in which the state of a system needs to ...
research
09/18/2014

Why Local Search Excels in Expression Simplification

Simplifying expressions is important to make numerical integration of la...

Please sign up or login with your details

Forgot password? Click here to reset