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arXiv Machine Learning By M. Harper Langston, Pierre-David Letourneau, Dalton Jones, Harris Teague, Peter Scott, Mingu Lee, Richard Lethin, Chris Lott, Roland Memisevic, Fatih Porikli

Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT

Read the original on arXiv Machine Learning →

The paper introduces a new approach to solving the MAX‑CUT problem by combining Particle Polynomial Relaxation (PPR) with a spectral graph‑coarsening technique called Combinatorial Multigrid (CMG). CMG provides fast, structure‑aware initial solutions that warm‑start PPR, enabling the algorithm to escape many local optima inherent in the binary optimization landscape. Experiments on benchmark instances show that the CMG+PPR pipeline achieves over 97 % of the known optimum on average, reaches the exact optimum on all sixty Biq Mac instances when augmented with discrete local‑search, and scales to graphs with up to 20,000 vertices while dramatically reducing memory usage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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