Derives accessible O(κΦ ln(κΦ ||w*||/ε)) iteration bound for rPDHG on unique-optima LPs, with computable Φ, two-stage performance, and equivalence to stability and sharpness.
arXiv preprint arXiv:2311.07710
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A modular CPU-GPU batching framework for branch-and-bound delivers 10-100x speedups with zero optimality gap when certifying optimal cardinality-constrained GLMs.
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Accessible Complexity Bounds for Restarted PDHG on Linear Programs with a Unique Optimizer
Derives accessible O(κΦ ln(κΦ ||w*||/ε)) iteration bound for rPDHG on unique-optima LPs, with computable Φ, two-stage performance, and equivalence to stability and sharpness.
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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.