Stochastic integer optimization has sample complexity that matches, undercuts, or exceeds the continuous case based on objective structure, with new tight bounds for nonconvex continuous problems.
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Adaptive multi-criteria scoring with online logistic regression for Benders subproblem selection yields statistically significant runtime and integral improvements on 135 survivable network design instances.
A parallelized branch-and-bound with isomorphism pruning algorithm achieves linear speedups for OA(128,9,2,4) and OA(144,9,2,4) and classifies all non-OD-equivalent OA(192,k,2,4) for k=9,10,11 for the first time.
citing papers explorer
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Sample Complexity of Stochastic Optimization with Integer Variables
Stochastic integer optimization has sample complexity that matches, undercuts, or exceeds the continuous case based on objective structure, with new tight bounds for nonconvex continuous problems.
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Adaptive Subproblem Selection in Benders Decomposition for Survivable Network Design Problems
Adaptive multi-criteria scoring with online logistic regression for Benders subproblem selection yields statistically significant runtime and integral improvements on 135 survivable network design instances.
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Parallelizing the branch-and-bound with isomorphism pruning algorithm for classifying orthogonal arrays
A parallelized branch-and-bound with isomorphism pruning algorithm achieves linear speedups for OA(128,9,2,4) and OA(144,9,2,4) and classifies all non-OD-equivalent OA(192,k,2,4) for k=9,10,11 for the first time.