Radiotherapy-Optimization/QP-Benchmark
QP-Benchmark Benchmark instances for Warm-IP, an open-source solver for large-scale convex quadratic programs (QPs), from the paper Warm-IP: A Path-Following ADMM Warm Start for Interior-Point Quadratic Programming (Aslani, Tefagh, Jhanwar, Zarepisheh; preprint link to be added). Every instance is a convex QP minimize 0.5 x'Qx + q'x + c subject to constraints (one-sided or two-sided; see below) stored as an HDF5 (.h5) file, one folder per family: MPC_data/ 64… See the full description on the dataset page: https://huggingface.co/datasets/Radiotherapy-Optimization/QP-Benchmark.
QP-Benchmark
Benchmark instances for [Warm-IP](https://github.com/Radiotherapy-Optimization/Warm-IP), an open-source solver for large-scale convex quadratic programs (QPs), from the paper Warm-IP: A Path-Following ADMM Warm Start for Interior-Point Quadratic Programming (Aslani, Tefagh, Jhanwar, Zarepisheh; preprint link to be added). Every instance is a convex QP
minimize 0.5 x'Qx + q'x + c subject to constraints (one-sided or two-sided; see below)
stored as an HDF5 (.h5) file, one folder per family:
MPCdata/ 64 model-predictive-control instances MPC001....h5 MMdata/ 137 Maros-Meszaros instances MM001....h5 IMRTlungdata/ 60 radiotherapy (lung IMRT) instances IMRTlung001.h5
Radiotherapy data is organized one folder per (modality, site) family -- IMRT_lung today; future releases may add sibling families such as IMRT_prostate or VMAT_lung -- and radiotherapy instance names are exactly <family>_<index>. Everything else about an instance (patient, treatment protocol, provenance) lives in the manifest (instances_metadata.csv, one row per instance: family, name, source, sizes, nonzero counts, protocol, the spelled-out problem formulation, note) and in each file's data_note, never in folder or file names.
Using the data
The Warm-IP repository downloads these files automatically (its src/data_loader.py fetches any requested instance on first use), so nothing needs to be downloaded by hand. To fetch files directly:
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="Radiotherapy-Optimization/QP-Benchmark", repo_type="dataset",
filename="IMRT_lung_data/IMRT_lung_001.h5")File format
Q (n x n, full symmetric) and the constraint matrix G (m x n) are stored in CSR form as four datasets each (Q_data, Q_indices, Q_indptr, Q_shape, and likewise for G); q and c are stored directly. Two constraint forms are used:
- Canonical (radiotherapy files) -- dataset
his present:G x <= h. - Two-sided (MM and MPC files) -- datasets
l_h,u_h,l_x,u_xare present:l_h <= G x <= u_h,l_x <= x <= u_x, with infinite entries encoding absent bounds; a row withl_h[i] == u_h[i]is an equality constraint.
Every file carries format_note (format description) and data_note (provenance). See the data documentation in the Warm-IP repository for full details, family descriptions, and references.
Sources
- MPC: the qpbenchmark MPC test set (Caron et al., 2024), qpsolvers/mpc_qpbenchmark.
- MM: the Maros-Meszaros convex QP test set (Maros and Meszaros, 1999), via qpsolvers/maros_meszaros_qpbenchmark.
- IMRT_lung: fluence-map-optimization QPs built from the public lung-patient data of PortPy (Jhanwar et al., 2023), one instance per patient under the
Lung_2Gy_30Fxprotocol.
Citing
If you use this data, please cite the Warm-IP paper (BibTeX in the repository README) and the original sources above for the family you use.
