Optimal Bounds for the $k$-cut Problem

In the $k$-cut problem, we want to find the smallest set of edges whose deletion breaks a given (multi)graph into $k$ connected components. Algorithms of Karger & Stein and Thorup showed how to find such a minimum $k$-cut in time approximately $O(n^{2k})$. The best lower bounds come from conjectures about the solvability of the $k$-clique problem, and show that solving $k$-cut is likely to require time $\Omega(n^k)$. Recent results of Gupta, Lee & Li have given special-purpose algorithms that solve the problem in time $n^{1.98k + O(1)}$, and ones that have better performance for special classes of graphs (e.g., for small integer weights). In this work, we resolve the problem for general graphs, by showing that the Contraction Algorithm of Karger outputs any fixed $k$-cut of weight $\alpha \lambda_k$ with probability $\Omega_k(n^{-\alpha k})$, where $\lambda_k$ denotes the minimum $k$-cut size. This also gives an extremal bound of $O_k(n^k)$ on the number of minimum $k$-cuts and an algorithm to compute a minimum $k$-cut in similar runtime. Both are tight up to lower-order factors, with the algorithmic lower bound assuming hardness of max-weight $k$-clique. The first main ingredient in our result is a fine-grained analysis of how the graph shrinks -- and how the average degree evolves -- in the Karger process. The second ingredient is an extremal bound on the number of cuts of size less than $2 \lambda_k/k$, using the Sunflower lemma.