Dataset for Towards Foundation Models for Mixed Integer Linear Programming MILP Evolve is a large scale dataset of Mixed Integer Linear Programming (MILP) problem classes and instances. It is generated using an LLM based evolutionary framework capable of producing a diverse set of MILP classes with unlimited instances. The dataset is designed to facilitate research in developing foundation models for MILP that generalize across problem classes. It supports multiple learning tasks, including integrality gap prediction, learning to branch, and aligning MILP instances with natural language descriptions. Our source code can be found on Github. It was proposed in the paper Towards Foundation Models for Mixed Integer Linear Programming. MILP Evolve MILP Classes MILP Classes Code Representation The seed and generated MILP Classes can be found at ./milp code/[evolve/seed] tab1 and ./milp code/[evolve/seed] tab2 correspond to Table 1 and 2 of our paper. MILP Instances for each class One can set different seeds to generate multiple instances for each class by changing the seed = 42 line in each code to seed = . We adopt mps format) and provide up to 1000 instances per class in the ./instance…
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