An Optimal Algorithm for the Indirect Covering Subtree Problem

TL;DR

This paper introduces an optimal $O(n \,\log n)$ algorithm for the indirect covering subtree problem on trees, improving previous solutions and establishing a lower bound, thus providing the fastest known method for related coverage problems.

Contribution

The authors develop the fastest known algorithm for the indirect covering subtree problem with a matching lower bound, improving upon previous algorithms and extending to related coverage problems.

Findings

Achieved $O(n \,\log n)$ time complexity for the problem.

Proved a lower bound of $\,\Omega(n \,\log n)$, establishing optimality.

Extended results to related coverage location problems.

Abstract

We consider the indirect covering subtree problem (Kim et al., 1996). The input is an edge weighted tree graph along with customers located at the nodes. Each customer is associated with a radius and a penalty. The goal is to locate a tree-shaped facility such that the sum of setup and penalty cost is minimized. The setup cost equals the sum of edge lengths taken by the facility and the penalty cost is the sum of penalties of all customers whose distance to the facility exceeds their radius. The indirect covering subtree problem generalizes the single maximum coverage location problem on trees where the facility is a node rather than a subtree. Indirect covering subtree can be solved in $O(n\log^2 n)$ time (Kim et al., 1996). A slightly faster algorithm for single maximum coverage location with a running time of $O(n\log^2n/\log\log n)$ has been provided (Spoerhase and Wirth, 2009). We achieve time $O(n\log n)$ for indirect covering subtree thereby providing the fastest known algorithm for both problems. Our result implies also faster algorithms for competitive location problems such as $(1,X)$-medianoid and $(1,p)$-centroid on trees. We complement our result by a lower bound of $\Omega(n\log n)$ for single maximum coverage location and $(1,X)$-medianoid on a real-number RAM model showing that our algorithm is optimal in running time.