Viresh Patel
Queen Mary
A domination algorithm for {0,1}-instances of the travelling salesman problem
The development of approximation algorithms for NP-hard combinatorial
optimization problems is a very active field of study. One usually measures
the performance of an approximation algorithm with the approximation ratio.
In this talk, I will begin by introducing a less well-known measure called
the domination ratio.
I will discuss an approximation algorithm for {0,1}-instances of the
travelling salesman problem which performs well with respect to domination
ratio. More precisely, given a {0,1}-edge-weighting of the complete graph
K_n on n vertices, our algorithm outputs a Hamilton cycle H of K_n with the
following property: the proportion of Hamilton cycles of K_n whose weight is
smaller than that of H is at most n^{-1/29} = o(1). We use a combination of
probabilistic and algorithmic techniques together with some results from
extremal graph theory. Previously, the best result in this direction was a
polynomial-time algorithm for general TSP that outputs a Hamilton cycle H
such that at most 1/2 of all Hamilton cycles of K_n have weight smaller than
H.
On the hardness side we can show that, if the Exponential Time Hypothesis
holds, there exists a constant C such that n^{-1/29} cannot be replaced
by \exp(-(\log n)^C) in our result above.
(Joint work with Daniela Kuehn and Deryk Osthus.)