On approximate data reduction for the Rural Postman Problem: Theory and experiments

TL;DR

This paper introduces a polynomial-size approximate kernelization scheme for the Rural Postman Problem, enabling significant data reduction while approximately preserving solution quality, supported by theoretical proofs and experimental validation.

Contribution

It presents the first polynomial-size approximate kernelization scheme for RPP, combining theoretical insights with practical data reduction techniques.

Findings

Data reduction to about 50% of vertices with minimal solution quality loss

Theoretical proof of WK[1]-completeness for RPP parameterized by additional edges and cost

Experimental validation on benchmark and real-world instances

Abstract

Given an undirected graph with edge weights and a subset $R$ of its edges, the Rural Postman Problem (RPP) is to find a closed walk of minimum total weight containing all edges of $R$. We prove that RPP is WK[1]-complete parameterized by the number and cost $d$ of edges traversed additionally to the required ones. Thus, in particular, RPP instances cannot be polynomial-time compressed to instances of size polynomial in $d$ unless the polynomial-time hierarchy collapses. In contrast, denoting by $b\leq 2d$ the number of vertices incident to an odd number of edges of $R$ and by $c\leq d$ the number of connected components formed by the edges in $R$, we show how to reduce any RPP instance $I$ to an RPP instance $I'$ with $2b+O(c/\varepsilon)$ vertices in $O(n^3)$ time so that any $\alpha$-approximate solution for $I'$ gives an $\alpha(1+\varepsilon)$-approximate solution for $I$, for any $\alpha\geq 1$ and $\varepsilon>0$. That is, we provide a polynomial-size approximate kernelization scheme (PSAKS). We experimentally evaluate it on wide-spread benchmark data sets as well as on two real snow plowing instances from Berlin. On instances with few connected components, the number of vertices and required edges is reduced to about $50\,\%$ at a $1\,\%$ solution quality loss. We also make first steps towards a PSAKS for the parameter $c$.

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