Approximate k-Steiner Forests Via the Lagrangian Relaxation Technique with Internal Preprocessing

Abstract

An instance of the k-Steiner forest problem consists of an undirected graph G = (V,E), the edges of which are associated with non-negative costs, and a collection ${\cal D} = \{ (s_i, t_i) : 1 \leq i \leq d \}$ of distinct pairs of vertices, interchangeably referred to as demands. We say that a forest ${\cal F} \subseteq G$ connects a demand (s _i , t _i ) when it contains an s _i -t _i path. Given a requirement parameter $k \leq |{\cal D}|$, the goal is to find a minimum cost forest that connects at least k demands in ${\cal D}$. This problem has recently been studied by Hajiaghayi and Jain [SODA ’06], whose main contribution in this context was to relate the inapproximability of k-Steiner forest to that of the dense k -subgraph problem. However, Hajiaghayi and Jain did not provide any algorithmic result for the respective settings, and posed this objective as an important direction for future research.

In this paper, we present the first non-trivial approximation algorithm for the k-Steiner forest problem, which is based on a novel extension of the Lagrangian relaxation technique. Specifically, our algorithm constructs a feasible forest whose cost is within a factor of $O( {\rm min} \{ n^{ 2/3 }, \sqrt{d} \} \cdot \log d )$ of optimal, where n is the number of vertices in the input graph and d is the number of demands.

Due to space limitations, some proofs and technical details are omitted from this extended abstract. We refer the reader to the full version of this paper (currently available online at http://www.math.tau.ac.il/~segevd), in which all missing information is provided.