Degree-Bounded Generalized Polymatroids and Approximating the Metric Many-Visits TSP

TL;DR

This paper extends matroid basis problems to generalized polymatroids with element multiplicities, providing approximation algorithms that improve solutions for complex combinatorial optimization problems like the metric Many-Visits TSP.

Contribution

It introduces a new algorithm for bounded degree g-polymatroid problems with multiplicities, achieving the same approximation guarantees as prior matroid-based methods.

Findings

Provides a 1.5-approximation for the metric Many-Visits TSP.

Extends previous algorithms to g-polymatroids with multiplicities.

Maintains approximation guarantees similar to Christofides' algorithm.

Abstract

In the Bounded Degree Matroid Basis Problem, we are given a matroid and a hypergraph on the same ground set, together with costs for the elements of that set as well as lower and upper bounds $f(\varepsilon)$ and $g(\varepsilon)$ for each hyperedge $\varepsilon$. The objective is to find a minimum-cost basis $B$ such that $f(\varepsilon) \leq |B \cap \varepsilon| \leq g(\varepsilon)$ for each hyperedge $\varepsilon$. Kir\'aly et al. (Combinatorica, 2012) provided an algorithm that finds a basis of cost at most the optimum value which violates the lower and upper bounds by at most $2 \Delta-1$, where $\Delta$ is the maximum degree of the hypergraph. When only lower or only upper bounds are present for each hyperedge, this additive error is decreased to $\Delta-1$. We consider an extension of the matroid basis problem to generalized polymatroids, or g-polymatroids, and additionally allow element multiplicities. The Bounded Degree g-polymatroid Element Problem with Multiplicities takes as input a g-polymatroid $Q(p,b)$ instead of a matroid, and besides the lower and upper bounds, each hyperedge $\varepsilon$ has element multiplicities $m_\varepsilon$. Building on the approach of Kir\'aly et al., we provide an algorithm for finding a solution of cost at most the optimum value, having the same additive approximation guarantee. As an application, we develop a $1.5$-approximation for the metric Many-Visits TSP, where the goal is to find a minimum-cost tour that visits each city $v$ a positive $r(v)$ number of times. Our approach combines our algorithm for the Bounded Degree g-polymatroid Element Problem with Multiplicities with the principle of Christofides' algorithm from 1976 for the (single-visit) metric TSP, whose approximation guarantee it matches.