README.md

metadata

license: mit
pretty_name: remilp-data

ReMILP dataset

MILP instances as bipartite variable-constraint graphs, with the labels of the downstream tasks of the ReMILP paper.

directory	instances	splits	labels
milp_evolve/	42,193	pretrain/{train,val}, gap/{train,val,test}	integrality gap
dmiplib/<CLASS>/<difficulty>/	300 per pair (23 pairs)	train, val, test = 200 / 50 / 50	solution pool

Pairs, from Distributional MIPLIB: CA and IS easy/medium; SC, VC and GISP easy/medium/hard; MMCN medium-BI, hard-BI and medium-BC; CFLP easy/medium; OTS easy/medium/hard; NNV easy; LB hard.

Layout

<source>/manifest.json                {"source", "processing", "entries": [{"id", "split", "num_nodes", "num_edges"}, ...]}
<source>/<split>/graphs/<id>.pt.zst   a torch_geometric HeteroData, zstd-compressed
<source>/<split>/raw.tar              instances/<id>.(mps|lp).gz and labels/<id>.json.gz

Labels

• milp_evolve: lp_ip_gap = min(1, |IP - LP| / (max(|IP|, |LP|) + 1e-5)).
• dmiplib: a pool of up to 300 solutions per instance (Gurobi, one hour per instance). solution_probs and active_probs are softmax-over-objective weighted averages over the pool of each binary variable's value and of each constraint's tightness.

Use

hf download orailix/remilp-data --repo-type dataset --local-dir data --exclude "*/raw.tar"

Attribution

The instances in this dataset come from D-MIPLIB and MILP-Evolve. All instances are re-distributed under their original licenses. Refer to DATA_LICENSE.md for more details.