A faster, better approximation algorithm for the minimum latency problem

We give a 7.18-approximation algorithm for the minimum latency problem that uses only O(n log n) calls to the prize-collecting Steiner tree (PCST) subroutine of Goemans and Williamson. This improves the previous best algorithms in both performance guarantee and running time. A previous algorithm of Goemans and Kleinberg for the minimum latency problem requires an approximation algorithm for the k-minimum spanning tree (k-MST) problem which is called as a black box for each value of k. Their algorithm can achieve an approximation factor of 10.77 while making O(n(n + log C) log n) PCST calls, a factor of 8.98 using O(n(3)(n + log C) log n) PCST calls, or a factor of 7.18 + epsilon using n(O(1/epsilon)) log C PCST calls, via the k-MST algorithms of Garg, Arya and Ramesh, and Arora and Karakostas, respectively. Here n denotes the number of nodes in the instance, and C is the largest edge cost in the input. In all cases, the running time is dominated by the PCST calls. Since the PCST subroutine can be implemented to run in O(n(2)) time, the overall running time of our algorithm is O(n3 log n). We also give a faster randomized version of our algorithm that achieves the same approximation guarantee in expectation, but uses only O(log(2) n) PCST calls, and derandomize it to obtain a deterministic algorithm with factor 7.18 + epsilon, using O(1/epsilon log(2) n) PCST calls. The basic idea for our improvement is that we do not treat the k-MST algorithm as a black box. This allows us to take advantage of some special situations in which the PCST subroutine delivers a 2-approximate k-MST. We are able to obtain the same approximation ratio that would be given by Goemans and Kleinberg if we had access to 2-approximate k-MSTs for all values of k, even though we have them only for some values of k that we are not able to specify in advance. We also extend our algorithm to a weighted version of the minimum latency problem.