Distributional MIPLIB
The first comprehensive standardized dataset for evaluating ML-guided MILP methods
The first comprehensive standardized dataset for evaluating ML-guided MILP methods
Distributional MIPLIB [arXiv Preprint] is the first comprehensive standardized dataset for evaluating ML-guided Mixed Integer Linear Programming (MILP) solving. It contains MILP distributions from 13 domains, classified into different hardness levels.
MILP is a fundamental tool for modeling combinatorial optimization problems. Recently, a growing body of research has used ML to accelerate MILP solving. Despite the increasing popularity of this approach, there is a lack of a common repository that provides distributions of similar MILP instances across different domains, at different hardness levels, with standardized test sets. We curate MILP distributions from existing work in this area as well as real-world problems that have not been used.
Note (*): NNV contains 588 test instances. MIRP contains 20 test instances. SRPN contains 22 and 20 test instances in the Easy and Hard group, respectively.
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