Approximating k-Forest with Resource Augmentation: A Primal-Dual Approach

TL;DR

This paper introduces a primal-dual algorithm that approximates the k-forest problem within a constant factor using resource augmentation, overcoming its inherent computational hardness.

Contribution

It presents the first polynomial-time algorithm that achieves constant-factor approximation for k-forest with resource augmentation, using a novel demand-removal perspective.

Findings

Algorithm removes at most m-k demands with cost within O(1/ε^2) of optimal.

Resource augmentation enables constant-factor approximation despite worst-case hardness.

Restating the problem in terms of unconnected demands offers new analytical insights.

Abstract

In this paper, we study the $k$-forest problem in the model of resource augmentation. In the $k$-forest problem, given an edge-weighted graph $G(V,E)$, a parameter $k$, and a set of $m$ demand pairs $\subseteq V \times V$, the objective is to construct a minimum-cost subgraph that connects at least $k$ demands. The problem is hard to approximate---the best-known approximation ratio is $O(\min\{\sqrt{n}, \sqrt{k}\})$. Furthermore, $k$-forest is as hard to approximate as the notoriously-hard densest $k$-subgraph problem. While the $k$-forest problem is hard to approximate in the worst-case, we show that with the use of resource augmentation, we can efficiently approximate it up to a constant factor. First, we restate the problem in terms of the number of demands that are {\em not} connected. In particular, the objective of the $k$-forest problem can be viewed as to remove at most $m-k$ demands and find a minimum-cost subgraph that connects the remaining demands. We use this perspective of the problem to explain the performance of our algorithm (in terms of the augmentation) in a more intuitive way. Specifically, we present a polynomial-time algorithm for the $k$-forest problem that, for every $\epsilon>0$, removes at most $m-k$ demands and has cost no more than $O(1/\epsilon^{2})$ times the cost of an optimal algorithm that removes at most $(1-\epsilon)(m-k)$ demands.