An Experimental Evaluation of the Best-of-Many Christofides’ Algorithm for the Traveling Salesman Problem

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9294)

Abstract

Recent papers on approximation algorithms for the traveling salesman problem (TSP) have given a new variant on the well-known Christofides’ algorithm for the TSP, called the Best-of-Many Christofides’ algorithm. The algorithm involves sampling a spanning tree from the solution to the standard LP relaxation of the TSP, and running Christofides’ algorithm on the sampled tree. In this paper we perform an experimental evaluation of the Best-of-Many Christofides’ algorithm to see if there are empirical reasons to believe its performance is better than that of Christofides’ algorithm. In our experiments, all of the implemented variants of the Best-of-Many Christofides’ algorithm perform significantly better than Christofides’ algorithm; an algorithm that samples from a maximum entropy distribution over spanning trees seems to be particularly good.

Keywords

traveling salesman problem Christofides algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceCornell UniversityIthacaUSA
  2. 2.School of Operations Research and Information EngineeringCornell UniversityIthacaUSA

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