[edit]
A Scalable Frank-Wolfe-Based Algorithm for the Max-Cut SDP
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:27822-27839, 2023.
Abstract
We consider the problem of solving large-scale instances of the Max-Cut semidefinite program (SDP), i.e., optimizing a linear function over n×n positive semidefinite (PSD) matrices with unit diagonal. When the cost matrix is PSD, we show how to exactly reformulate the problem as maximizing a smooth concave function over PSD matrices with unit trace. By applying the Frank-Wolfe method, we obtain a simple algorithm that is compatible with recent sampling-based techniques to solve SDPs using low memory. We demonstrate the practical performance of our method on 106×106 instances of the max-cut SDP with costs having up to 5×106 non-zero entries. Theoretically, we show that our method solves problems with diagonally dominant costs to relative error ϵ in O(nϵ−1) calls to a randomized approximate largest eigenvalue subroutine, each of which succeeds with high probability after O(log(n)ϵ−1/2) matrix-vector multiplications with the cost matrix.