API

Graph

Attributes	Description
`Graph.structure`	Graph structure represented as a matrix of nodes separated by layers
`Graph.actual_layer` .	Represent the number of layers of de diagram

Methods	Description
`Graph.get_node_count() -> int`	Gets the number of node in the graph.
`Graph.get_arc_count() -> int`	Gets the number of arc in the graph.
`Graph.add_node(Node) -> void`	Add a node to the graph
`Graph.create_and_add_node(node_id, node_state) -> Node`	Creates de node and then it added to the graph
`Graph.add_new_layer() -> void`	Remove not active nodes in layer, construction top_down
`Graph.to_string() -> string`	String representation of the graph.

Attributes	Description
`DD.problem.` .	An instance of the problem class, used to create the decision diagram.

Methods	Description
`DD.create_decision_diagram(verbose) -> void`	Creates the exact decision diagram, and saves the construction time taken.
`DD.create_restricted_decision_diagram(max_width, verbose) -> void`	Creates the restricted decision diagram, and saves the construction time.
`DD.create_relax_priority_decision_diagram(max_width, verbose) -> void`	Creates a relaxed decision diagram by merging the lowest-priority nodes one by one, and records the construction time.
`DD.create_relax_grouping_decision_diagram(max_width, verbose) -> void`	Creates a relaxed decision diagram by merging nodes in groups by priority difference, and records the construction time.
`DD.reduce_decision_diagram(verbose) -> void`	Reduced decision diagram and stores it in the graph_DD attribute, and saves the reduction time.
`DD.export_graph_file(file_name)->void`	Exports the current decision diagram to a .GML file.
`DD.get_decision_diagram()-> Graph`	Returns the current decision diagram graph object
`DD.get_decision_diagram_copy()-> Graph`	Returns a copy of the decision diagram graph object, ensuring it is not a pointer to the original.
`DD.get_building_time()->void`	Returns the execution time of the DDBuilder.
`DD.get_reduction_time()->void`	Returns the execution time of the reduce of the diagram.

Methods	Description
`ShortestLongestPath.get_solution()->PathStructure`	Retruns the solution of the previously calculated path.
`ShortestLongestPath.set_parameters(weights, objective) -> void`	Sets the parameters of the path weights.
`ShortestLongestPath.solve() -> PathStructure`	Solves the given decision diagram using the previously set weights and objective.

Attributes	Description
`AbstractProblem.initial_state`	The initial state of the problem, which can be a list, integer, string, etc., depending on the specific problem requirements
`AbstractProblem.ordered_variables`	A list of variables sorted in the specific order that was given.
`AbstractProblem.variables_domain`	A dictionary mapping each variable to its domain.

Methods	Description
`AbstractProblem.transition_function(previous_state, variable_index, variable_value, scratch_state) -> bool feasibility`	Abstract method that must be implemented by subclasses to define the transition function. The new state is written into `scratch_state`; the return value indicates feasibility.
`AbstractProblem.get_priority_for_discard_node(state) -> int value`	Method that should be implemented by subclasses to define the priority function for discarding nodes.
`AbstractProblem.get_priority_for_merge_nodes(node_id, state) -> int value`	Method that should be implemented by subclasses to define the priority function for merging nodes.
`AbstractProblem.merge_operator(state_one, state_two) -> State state`	Method that should be implemented by subclasses to define the new state when two nodes are merged
`AbstractProblem.get_state_as_string(state) -> string state`	Method that should be implemented by subclasses to define a string representation of the state.
`AbstractProblem.get_state_copy -> 'State'`	Gets the copy of the state.
`AbstractProblem.get_final_state -> 'State'`	Gets the final state of the problem.

Methods	Description
`MaxFlow.get_time() -> double`	Returns the execution time of the max-flow algorithm.
`MaxFlow.get_max_flow() -> double`	Returns the precomputed values in the solve_max_flow() section.
`MaxFlow.solve_max_flow(capacities) -> double`	Solves the maximum flow problem in the graph with the provided arc capacities. Returns the maximum flow found by the algorith.
`MaxFlow.compute_visited_nodes() -> map`	Returns a dictionary with the nodes that are visited in the graph.

Methods	Description
`MaxFlowGurobi.get_time() -> double`	Method that returns the time taken to solve the problem.
`MaxFlowGurobi.get_max_flow() -> double`	Method that returns the maximum flow found by the algorithm.
`MaxFlowGurobi.solve_max_flow(capacities) -> double`	Method that solves and returns the maximum flow problem in a graph.

Methods	Description
`AbstractCutGenerator.get_time() -> double`	Returns the execution time of the cut algorithm.
`AbstractCutGenerator.get_cut() -> pair<vector<double, double>>`	Returns the precomputed values from the generate_cut() method.
`AbstractCutGenerator.generate_cut(x_values, verbose) -> bool`	Solves the decision diagram, finding a cut. Returns a boolean indicating whether the solution was successfully found.
`AbstractCutGenerator.get_name() -> string`	Returns the name of the cut generator.

Methods	Description
`CutStrengthening.get_time() -> double`	Returns the execution time of the strengthening algorithm.
`CutStrengthening.get_cut_strengthening() -> pair<vector<double>, double>`	Returns the precomputed values from the `cut_strengthening()` method.
`CutStrengthening.cut_strengthening(coefficients, constant) -> bool`	Strengthens the cut by improving the coefficients and right-hand side value. Returns a boolean indicating whether the solution was successfully found.