# Approximating Graphic Multi-Path TSP and Graphic Ordered TSP

## TL;DR

This paper presents a new approximation algorithm for the graphic Multi-Path TSP problem achieving a factor of 2, improving over previous bounds, and extends techniques to Ordered TSP with a 1.791-approximation.

## Contribution

It introduces a novel LP-based sampling approach for graphic Multi-Path TSP, achieving a better approximation factor of 2, and adapts these ideas to Ordered TSP for improved approximation.

## Key findings

- Achieves a 2-approximation for graphic Multi-Path TSP.
- Provides a 1.791-approximation for Ordered TSP in graphic metrics.
- Shows that below-2 approximation for a special case implies below-2 for the general problem.

## Abstract

The path version of the Traveling Salesman Problem is one of the most well-studied variants of the ubiquitous TSP. Its generalization, the Multi-Path TSP, has recently been used in the best known algorithm for path TSP by Traub and Vygen [Cambridge University Press, 2024]. The best known approximation factor for this problem is $2.214$ by B\"{o}hm, Friggstad, M\"{o}mke and Spoerhase [SODA 2025]. In this paper we show that for the case of graphic metrics, a significantly better approximation guarantee of $2$ can be attained. Our algorithm is based on sampling paths from a decomposition of the flow corresponding to the optimal solution to the LP for the problem, and connecting the left-out vertices with doubled edges. The cost of the latter is twice the optimum in the worst case; we show how the cost of the sampled paths can be absorbed into it without increasing the approximation factor. Furthermore, we prove that any below-$2$ approximation algorithm for the special case of the problem where each source is the same as the corresponding sink yields a below-$2$ approximation algorithm for Graphic Multi-Path TSP. We also show that our ideas can be utilized to give a factor $1.791$-approximation algorithm for Ordered TSP in graphic metrics, for which the aforementioned paper [SODA 2025] and Armbruster, Mnich and N\"agele [APPROX 2024] give a $1.868$-approximation algorithm in general metrics.