The Itinerant List Update Problem

N. Olver, K. Pruhs, K. Schewior, R. Sitters and L. Stougie
Proceedings of the 16th Workshop on Approximation and Online Algorithms (WAOA)

Approximate Multi-Matroid Intersection via Iterative Refinement

André Linhares, Neil Olver, Chaitanya Swamy, Rico Zenklusen
Preprint (arXiv:1811.09027)

We introduce a new iterative rounding technique to round a point in a matroid polytope subject to further matroid constraints. This technique returns an independent set in one matroid with limited violations of the other ones. On top of the classical steps of iterative relaxation approaches, we iteratively refine/split involved matroid constraints to obtain a more restrictive constraint system, that is amenable to iterative relaxation techniques. Hence, throughout the iterations, we both tighten constraints and later relax them by dropping constrains under certain conditions. Due to the refinement step, we can deal with considerably more general constraint classes than existing iterative relaxation/rounding methods, which typically round on one matroid polytope with additional simple cardinality constraints that do not overlap too much.

We show how our rounding method, combined with an application of a matroid intersection algorithm, yields the first 2-approximation for finding a maximum-weight common independent set in 3 matroids. Moreover, our 2-approximation is LP-based, and settles the integrality gap for the natural relaxation of the problem. Prior to our work, no better upper bound than 3 was known for the integrality gap, which followed from the greedy algorithm. We also discuss various other applications of our techniques, including an extension that allows us to handle a mixture of matroid and knapsack constraints.

A Duality-based 2-approximation algorithm for maximum agreement forest

N. Olver, F. Schalekamp, S. van der Ster, L. Stougie and A. van Zuylen
Preprint (arXiv:1811.05916)

On the Integrality Gap of the Prize-Collecting Steiner Forest LP

Jochen Koenemann, Kanstantsin Pashkovich, Neil Olver, R. Ravi, Chaitanya Swamy, Jens Vygen
Accepted to APPROX 2017

Approximability of robust network design

Neil Olver and Bruce Shepherd
Mathematics of Operations Research 39(2):561–572, 2014. Conference version: SODA 2010

We consider robust network design problems where the set of feasible demands may be given by an arbitrary polytope or convex body more generally. This model, introduced by Ben-Ameur and Kerivin (2003), generalizes the well studied virtual private network (VPN) problem. Most research in this area has focused on finding constant factor approximations for specific polytope of demands, such as the class of hose matrices used in the definition of VPN. As pointed out in Chekuri (2007), however, the general problem was only known to be APX-hard (based on a reduction from the Steiner tree problem). We show that the general robust design is hard to approximate to within logarithmic factors. We establish this by showing a general reduction of buy-at-bulk network design to the robust network design problem. In the second part of the paper, we introduce a natural generalization of the VPN problem. In this model, the set of feasible demands is determined by a tree with edge capacities; a demand matrix is feasible if it can be routed on the tree. We give a constant factor approximation algorithm for this problem that achieves factor 8 in general, and 2 for the case where the tree has unit capacities.