Downloading a QPLIB Instance#
The OMMX repository provides quadratic programming benchmark instances from QPLIB in OMMX Artifact format.
Note
More details: The QPLIB instances in OMMX Artifact format are hosted in the GitHub Container Registry for the OMMX repository (link).
QPLIB is a library of quadratic programming instances. For more information about QPLIB, see the QPLIB website.
Please see this page for information on GitHub Container Registry.
You can easily download these instances with the OMMX SDK, then directly use them as inputs to OMMX Adapters. For example, to solve the QPLIB_3514 instance (reference) with PySCIPOpt, you can:
Download the 3514 instance with
dataset.qplibfrom the OMMX Python SDK.Solve with PySCIPOpt via the OMMX PySCIPOpt Adapter.
Here is a sample Python code:
# OMMX Python SDK
from ommx import dataset
# OMMX PySCIPOpt Adapter
from ommx_pyscipopt_adapter import OMMXPySCIPOptAdapter
# Step 1: Download the 3514 instance from QPLIB
instance = dataset.qplib("3514")
# Step 2: Solve with PySCIPOpt via the OMMX PySCIPOpt Adapter
solution = OMMXPySCIPOptAdapter.solve(instance)
This makes it easy to benchmark quadratic programming solvers using the same QPLIB instances.
Note about Annotations with the Instance#
The downloaded instance includes various annotations accessible via the annotations property:
import pandas as pd
# Display annotations in tabular form using pandas
pd.DataFrame.from_dict(instance.annotations, orient="index", columns=["Value"]).sort_index()
| Value | |
|---|---|
| org.ommx.qplib.conscurvature | indefinite |
| org.ommx.qplib.convex | False |
| org.ommx.qplib.density | 0.0018696071160533081 |
| org.ommx.qplib.laghessianavgblocksize | 1.983607 |
| org.ommx.qplib.laghessianmaxblocksize | 61 |
| org.ommx.qplib.laghessianminblocksize | 1 |
| org.ommx.qplib.nbinvars | 15 |
| org.ommx.qplib.nboundedvars | 0 |
| org.ommx.qplib.nconcavenlcons | 0 |
| org.ommx.qplib.ncons | 1860 |
| org.ommx.qplib.ncontvars | 1800 |
| org.ommx.qplib.nconvexnlcons | 0 |
| org.ommx.qplib.ndiagquadcons | 0 |
| org.ommx.qplib.nindefinitenlcons | 900 |
| org.ommx.qplib.nintvars | 0 |
| org.ommx.qplib.njacobiannlnz | 2700 |
| org.ommx.qplib.njacobiannz | 5400 |
| org.ommx.qplib.nlaghessianblocks | 915 |
| org.ommx.qplib.nlaghessiandiagnz | 900 |
| org.ommx.qplib.nlaghessiannz | 2700 |
| org.ommx.qplib.nldensity | 0.001652892561983471 |
| org.ommx.qplib.nlincons | 960 |
| org.ommx.qplib.nlinfunc | 961 |
| org.ommx.qplib.nlnz | 2700 |
| org.ommx.qplib.nnlbinvars | 15 |
| org.ommx.qplib.nnlfunc | 900 |
| org.ommx.qplib.nnlintvars | 0 |
| org.ommx.qplib.nnlsemi | 0 |
| org.ommx.qplib.nnlvars | 1815 |
| org.ommx.qplib.nobjnlnz | 0 |
| org.ommx.qplib.nobjnz | 915 |
| org.ommx.qplib.nobjquaddiagnz | 0 |
| org.ommx.qplib.nobjquadnegev | 0 |
| org.ommx.qplib.nobjquadnz | 0 |
| org.ommx.qplib.nobjquadposev | 0 |
| org.ommx.qplib.nquadcons | 900 |
| org.ommx.qplib.nquadfunc | 900 |
| org.ommx.qplib.nsemi | 0 |
| org.ommx.qplib.nsingleboundedvars | 1800 |
| org.ommx.qplib.nsos1 | 0 |
| org.ommx.qplib.nsos2 | 0 |
| org.ommx.qplib.nvars | 1815 |
| org.ommx.qplib.nz | 6315 |
| org.ommx.qplib.objcurvature | linear |
| org.ommx.qplib.objquaddensity | 0 |
| org.ommx.qplib.objquadproblevfrac | 0 |
| org.ommx.qplib.objsense | min |
| org.ommx.qplib.objtype | linear |
| org.ommx.qplib.probtype | LMQ |
| org.ommx.qplib.solinfeasibility | 2.220446049e-16 |
| org.ommx.qplib.solobjvalue | 366.6218167 |
| org.ommx.qplib.solsource | Solution found by SCIP |
| org.ommx.qplib.url | http://qplib.zib.de/QPLIB_3514.html |
| org.ommx.v1.instance.authors | Stefan Vigerske |
| org.ommx.v1.instance.constraints | 1920 |
| org.ommx.v1.instance.created | 2025-10-02T22:45:08.030992+09:00 |
| org.ommx.v1.instance.dataset | QPLIB |
| org.ommx.v1.instance.license | CC-BY-4.0 |
| org.ommx.v1.instance.title | QPLIB_3514 |
| org.ommx.v1.instance.variables | 1815 |
These instances have both dataset-level annotations and dataset-specific annotations.
There are seven dataset-wide annotations with dedicated properties:
Annotation |
Property |
Description |
|---|---|---|
|
|
The authors of the instance |
|
|
The number of constraint conditions in the instance |
|
|
The date of the instance was saved as an OMMX Artifact |
|
|
The name of the dataset to which this instance belongs |
|
|
The license of this dataset |
|
|
The name of the instance |
|
|
The total number of decision variables in the instance |
QPLIB Annotations#
QPLIB instances include comprehensive annotations that describe the mathematical properties of quadratic programming problems. These annotations are based on the official QPLIB specification and are prefixed with org.ommx.qplib.*.
For detailed information about all available QPLIB annotations and their meanings, please refer to the official QPLIB documentation.
For example, you can check the problem type and objective curvature of the QPLIB instance:
# QPLIB-specific annotations
print(f"Problem type: {instance.annotations['org.ommx.qplib.probtype']}")
print(f"Objective type: {instance.annotations['org.ommx.qplib.objtype']}")
print(f"Objective curvature: {instance.annotations['org.ommx.qplib.objcurvature']}")
print(f"Number of variables: {instance.annotations['org.ommx.qplib.nvars']}")
print(f"Number of constraints: {instance.annotations['org.ommx.qplib.ncons']}")
Problem type: LMQ
Objective type: linear
Objective curvature: linear
Number of variables: 1815
Number of constraints: 1860