A modular CPU-GPU batching framework for branch-and-bound delivers 10-100x speedups with zero optimality gap when certifying optimal cardinality-constrained GLMs.
Ideal formulations for constrained convex optimization problems with indicator variables.Mathematical Programming, 192(1): 57–88
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From Sequential Nodes to GPU Batches: Parallel Branch and Bound for Optimal $k$-Sparse GLMs
A modular CPU-GPU batching framework for branch-and-bound delivers 10-100x speedups with zero optimality gap when certifying optimal cardinality-constrained GLMs.