LP Relaxation and Tree Packing for Minimum $k$-cuts

TL;DR

This paper revisits an LP relaxation for the $k$-cut problem, linking it to tree packings, improving algorithms, and providing new bounds and insights into the problem's structure.

Contribution

It shows the dual of the LP yields a tree packing, extends Karger's analysis to $k$-cuts, improves Thorup's algorithm runtime, and relates LP solutions to graph partitions.

Findings

Dual LP yields a tree packing for $k$-cut

Improved runtime of Thorup's algorithm by a factor of $n$

Bound on the integrality gap of the LP is $2(1-1/n)$

Abstract

Karger used spanning tree packings to derive a near linear-time randomized algorithm for the global minimum cut problem as well as a bound on the number of approximate minimum cuts. This is a different approach from his well-known random contraction algorithm. Thorup developed a fast deterministic algorithm for the minimum $k$-cut problem via greedy recursive tree packings. In this paper we revisit properties of an LP relaxation for $k$-cut proposed by Naor and Rabani, and analyzed by Chekuri, Guha and Naor. We show that the dual of the LP yields a tree packing, that when combined with an upper bound on the integrality gap for the LP, easily and transparently extends Karger's analysis for mincut to the $k$-cut problem. In addition to the simplicity of the algorithm and its analysis, this allows us to improve the running time of Thorup's algorithm by a factor of $n$. We also improve the bound on the number of $\alpha$-approximate $k$-cuts. Second, we give a simple proof that the integrality gap of the LP is $2(1-1/n)$. Third, we show that an optimum solution to the LP relaxation, for all values of $k$, is fully determined by the principal sequence of partitions of the input graph. This allows us to relate the LP relaxation to the Lagrangian relaxation approach of Barahona and Ravi and Sinha; it also shows that the idealized recursive tree packing considered by Thorup gives an optimum dual solution to the LP. This work arose from an effort to understand and simplify the results of Thorup.