Euiwoong Lee (이의웅), The Karger-Stein algorithm is optimal for k-cut

In the k-cut problem, we are given an edge-weighted graph and want to find the least-weight set of edges whose deletion breaks the graph into k connected components. It is easy to see that the elegant randomized contraction algorithm of Karger and Stein for global mincut (k=2) can be naturally extended for general k with the running time of $O(n^{2k-2})$. Using tree packings, Thorup gave a deterministic algorithm that has the same running time.

In this work, we show that for any fixed $k\ge 2$, the Karger-Stein algorithm outputs any fixed minimum k-cut with probability at least $\mathrm{\Omega }(n^{-k})$. This also gives an extremal bound of $O(n^k)$ on the number of minimum k-cuts in an n-vertex graph and an algorithm to compute a minimum k-cut in similar runtime. Both are essentially tight. The first main ingredient in our result is a fine-grained analysis of how the graph shrinks—and how the average degree evolves—under the Karger-Stein process. The second ingredient is an extremal result bounding the number of cuts of size at most $(2-\delta )OPT/k$, using the Sunflower lemma.

Joint work with Anupam Gupta, David G. Harris, and Jason Li.