corneto.methods.milp_carnival

corneto.methods.milp_carnival(G, perturbations, measurements, beta_weight=0.2, max_dist=None, penalize='edges', use_perturbation_weights=False, interaction_graph_attribute='interaction', disable_acyclicity=False, backend=<corneto.backend._cvxpy_backend.CvxpyBackend object>)

Build the supported single-condition CARNIVAL ILP formulation.

This simple formulation accepts one perturbation and measurement mapping. Use CarnivalILP for the general multi-condition formulation, or CarnivalFlow when a flow-based multi-condition model is required.

NOTE: Since the method is decoupled from specific solvers, the default pool of solutions generated using CPLEX is not available.

Parameters:

• G – The graph object representing the network.

• perturbations – Perturbations applied to vertices in the graph.

• measurements – Measured values for vertices in the graph.

• beta_weight (float) – The weight for the regularization term in the objective function.

• max_dist – The maximum distance allowed for vertex positions in the graph.

• penalize – The type of regularization to apply (‘nodes’, ‘edges’, or ‘both’).

• use_perturbation_weights – Whether to weight perturbations in the objective.

• interaction_graph_attribute – Graph attribute containing interaction signs.

• disable_acyclicity – Whether to disable the acyclicity constraint.

• backend – The backend engine to use for the optimization.

Returns:

The optimization problem object.