Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation

arXiv:2310.14161v2 Announce Type: replace Abstract: Machine learning has been successfully applied to improve the efficiency of Mixed-Integer Linear Programming (MILP) solvers. However, the learning-based solvers often suffer from severe performance degradation on unseen MILP instances -- especially on large-scale instances from a perturbed environment -- due to the limited diversity of training distributions. To tackle this problem, we propose a novel approach, which is called Adversarial Instance Augmentation and does not require to know the problem type for new instance generation, to promo

Source: arXiv cs.LG — read the full report at the original publisher.

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