On local elimination algorithms for sparse discrete optimization problems

We discuss local elimination algorithms that compute global information using local computations. Results of benchmarking show real computational capabilities of block elimination algorithms combined with SYMPHONY solver. Strategies for parallelizing a sequential local elimination algorithm for sparse discrete optimization problems are analyzed. We propose to use hybrid Master-Worker scheme where Worker processors (GPUs) solve concurrently subproblems corresponding to super-nodes of extended elimination tree that are generated by a single master process (CPU).