ommx.qplib

Functions

load_file(path) → ommx.Instance
load_solution(path, *, num_variables) → ommx.State	Load candidate values using ommx.State.load_qplib_solution().

Module Contents

load_file(path: str) → Instance

load_solution(path: str, *, num_variables: int) → State

Load candidate values using ommx.State.load_qplib_solution().

Evaluate a published candidate

Download the problem and candidate for QPLIB 0018 into your working directory. The problem defines the variables, their domains, the constraints, and the objective to minimize. The candidate assigns values to those variables. Use a matching pair of files so that the variable IDs in the assignment refer to the intended model. QPLIB credits Andrea Scozzari for this instance and distributes it under CC BY 4.0.

>>> from ommx import ConstraintKind, Instance
>>> from ommx.qplib import load_solution
>>> instance = Instance.load_qplib("QPLIB_0018.qplib")
>>> candidate = load_solution("QPLIB_0018.sol", num_variables=len(instance.decision_variables))
>>> solution = instance.evaluate(candidate, atol=1e-8)
>>> solution.feasible, round(solution.objective, 6)
(True, -6.386015)

Passing the variable count fills omitted coordinates with zero. Evaluation computes the objective from the model and checks variable domains and constraints at the given tolerance. The objective in the file is not an assignment to a variable or a proof of optimality. QPLIB 0018 minimizes its objective, so a smaller value is better among feasible candidates.

>>> variables = solution.decision_variables_df()
>>> selected = variables.loc[variables["value"] != 0, ["lower", "value", "upper"]]
>>> assert (selected["lower"] <= selected["value"]).all()

To see which constraint a different candidate violates, assign a value of zero for every variable. This problem requires their sum to be one.

>>> zeros = {v.id: 0.0 for v in instance.decision_variables}
>>> rejected = instance.evaluate(zeros, atol=1e-8)
>>> rejected.feasible, rejected.objective
(False, 0.0)
>>> rows = rejected.constraints_df(kind=ConstraintKind.Regular)
>>> rows.loc[~rows["feasible"], "violation"].tolist()
[1.0]

Filter on the feasibility flag to respect the numerical tolerance. ommx.Solution.decision_variables_df() shows the values and bounds for individual variables. If you also want to try a candidate as a starting point, see ommx_pyscipopt_adapter.OMMXPySCIPOptAdapter.solve().