On the metric s-t path Traveling Salesman Problem

TL;DR

This paper provides a unified analysis of approximation algorithms for the metric s-t path TSP, introducing a correction vector and comparing LP relaxations to improve understanding of approximation bounds.

Contribution

It unifies previous results by analyzing LP relaxations and introduces a correction vector to simplify and strengthen approximation guarantees for the s-t path TSP.

Findings

Both LP relaxations have the same fractional optimal value.

Integral solutions are within 1.5 times the LP optimal value.

A constructed instance shows limitations of certain approximation methods.

Abstract

We study the metric $s$-$t$ path Traveling Salesman Problem (TSP). [An, Kleinberg, and Shmoys, STOC 2012] improved on the long standing $\frac{5}{3}$-approximation factor and presented an algorithm that achieves an approximation factor of $\frac{1+\sqrt{5}}{2}\approx1.61803$. Later [Seb\H{o}, IPCO 2013] further improved the approximation factor to $\frac{8}{5}$. We present a simple, self-contained analysis that unifies both results; our main contribution is a \emph{unified correction vector}. We compare two different linear programming (LP) relaxations of the $s$-$t$ path TSP, namely, the path version of the Held-Karp LP relaxation for TSP and a weaker LP relaxation, and we show that both LPs have the same (fractional) optimal value. Also, we show that the minimum-cost of integral solutions of the two LPs are within a factor of $\frac{3}{2}$ of each other. We prove that a half-integral solution of the stronger LP-relaxation of cost $c$ can be rounded to an integral solution of cost at most $\frac{3}{2}c$. Additionally, we give a bad instance that presents obstructions to two obvious methods that aim for an approximation factor of $\frac{3}{2}$.