Max Cut and the Smallest Eigenvalue

We describe a new approximation algorithm for Max Cut. Our algorithm runs in $\tilde O(n^2)$ time, where $n$ is the number of vertices, and achieves an approximation ratio of $.531$. In instances in which an optimal solution cuts a $1-\varepsilon$ fraction of edges, our algorithm finds a solution that cuts a $1-4\sqrt{\varepsilon} + 8\varepsilon-o(1)$ fraction of edges. Our main result is a variant of spectral partitioning, which can be implemented in nearly linear time. Given a graph in which the Max Cut optimum is a $1-\varepsilon$ fraction of edges, our spectral partitioning algorithm finds a set $S$ of vertices and a bipartition $L,R=S-L$ of $S$ such that at least a $1-O(\sqrt \varepsilon)$ fraction of the edges incident on $S$ have one endpoint in $L$ and one endpoint in $R$. (This can be seen as an analogue of Cheeger's inequality for the smallest eigenvalue of the adjacency matrix of a graph.) Iterating this procedure yields the approximation results stated above. A different, more complicated, variant of spectral partitioning leads to a polynomial time algorithm that cuts a $1/2 + e^{-\Omega(1/\varepsilon)}$ fraction of edges in graphs in which the optimum is $1/2 + \varepsilon$. [PUBLICATION ABSTRACT]