Built-in Algorithms

ScipAlgorithm

Bases: BaseAlgorithmSync

Classical exact optimization algorithm using SCIP (Solving Constraint Integer Programs).

This algorithm wraps the SCIP solver to provide exact solutions for optimization problems using classical branch-and-bound methods. It translates Luna quantum models to LP format, solves them with SCIP, and translates the results back.

Parameters:

max_runtime –

Defines the maximum runtime for the SCIP solver in seconds, defaults to 1 hour.
quiet_output –

Defines the verbosity of the SCIP solver output.
_logger –

Class-level logger for tracking algorithm execution.

Raises:

InfeasibleModelError: If the model has no feasible solution. –

Requires

Install the 'pre-defined' extra: pip install luna-bench[pre-defined]

`run(model: Model) -> Solution`

Solve an optimization model using the SCIP classical solver.

Parameters:

model (Model) –

The Luna optimization model to solve.

Returns:

Solution –

Solution object containing the optimal variable assignments, objective value, and timing information.

Raises:

InfeasibleModelError –

If SCIP determines the model is infeasible.

Examples:

>>> scip_algo = ScipAlgorithm()
>>> solution = scip_algo.run(my_model)

FakeAlgorithm

Bases: BaseAlgorithmSync

Fake algorithm that does nothing.

This algorithm is used in the development process.

`run(model: Model) -> Solution`

Run a fake algorithm, which will sleep for a random amount of time.