| --- |
| license: apache-2.0 |
| pretty_name: QP-Benchmark |
| tags: |
| - quadratic-programming |
| - optimization |
| - radiotherapy |
| - 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: |
|
|
| MPC_data/ 64 model-predictive-control instances MPC_001_....h5 |
| MM_data/ 137 Maros-Meszaros instances MM_001_....h5 |
| IMRT_lung_data/ 60 radiotherapy (lung IMRT) instances IMRT_lung_001.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](https://github.com/Radiotherapy-Optimization/Warm-IP) |
| 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: |
|
|
| ```python |
| 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 `h` is present: |
| `G x <= h`. |
| * **Two-sided (MM and MPC files)** -- datasets `l_h`, `u_h`, `l_x`, |
| `u_x` are present: `l_h <= G x <= u_h`, `l_x <= x <= u_x`, with |
| infinite entries encoding absent bounds; a row with `l_h[i] == u_h[i]` |
| is an equality constraint. |
|
|
| Every file carries `format_note` (format description) and `data_note` |
| (provenance). See the |
| [data documentation](https://github.com/Radiotherapy-Optimization/Warm-IP/blob/main/data/README.md) |
| 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](https://github.com/qpsolvers/mpc_qpbenchmark). |
| * **MM**: the Maros-Meszaros convex QP test set (Maros and Meszaros, |
| 1999), via |
| [qpsolvers/maros_meszaros_qpbenchmark](https://github.com/qpsolvers/maros_meszaros_qpbenchmark). |
| * **IMRT_lung**: fluence-map-optimization QPs built from the public |
| lung-patient data of |
| [PortPy](https://github.com/PortPy-Project/PortPy) (Jhanwar et al., |
| 2023), one instance per patient under the `Lung_2Gy_30Fx` protocol. |
| |
| ## Citing |
| |
| If you use this data, please cite the Warm-IP paper (BibTeX in the |
| [repository README](https://github.com/Radiotherapy-Optimization/Warm-IP#citing)) |
| and the original sources above for the family you use. |
| |
