corneto.methods.PrizeCollectingSteinerTree

class corneto.methods.PrizeCollectingSteinerTree(include_all_terminals=False, max_flow=None, default_edge_cost=1.0, flow_name='_flow', root_vertex=None, root_selection_strategy='first', best_root_candidates='data', epsilon=1, strict_acyclic=True, disable_structured_sparsity=False, in_flow_edge_type=EdgeType.DIRECTED, out_flow_edge_type=EdgeType.DIRECTED, lambda_reg=0.0, force_flow_through_root=False, backend=None)

Bases: FlowMethod

Prize-Collecting Steiner Tree optimization method built on top of FlowMethod.

In a prize-collecting Steiner tree problem, terminals can have prizes (values > 0), making them optional terminals that provide a benefit if included in the solution. The class supports providing multiple max_flow and root_vertex values per sample.

Parameters:

• include_all_terminals (bool)

• max_flow (float | List[float] | None)

• default_edge_cost (float)

• flow_name (str)

• root_vertex (Any | List[Any] | None)

• root_selection_strategy (Literal['first', 'best'])

• best_root_candidates (Literal['data', 'graph'])

• epsilon (float)

• strict_acyclic (bool)

• disable_structured_sparsity (bool)

• in_flow_edge_type (EdgeType)

• out_flow_edge_type (EdgeType)

• lambda_reg (float)

• force_flow_through_root (bool)

• backend (Backend | None)

__init__(include_all_terminals=False, max_flow=None, default_edge_cost=1.0, flow_name='_flow', root_vertex=None, root_selection_strategy='first', best_root_candidates='data', epsilon=1, strict_acyclic=True, disable_structured_sparsity=False, in_flow_edge_type=EdgeType.DIRECTED, out_flow_edge_type=EdgeType.DIRECTED, lambda_reg=0.0, force_flow_through_root=False, backend=None)

Parameters:

• include_all_terminals (bool)

• max_flow (float | List[float] | None)

• default_edge_cost (float)

• flow_name (str)

• root_vertex (Any | List[Any] | None)

• root_selection_strategy (Literal['first', 'best'])

• best_root_candidates (Literal['data', 'graph'])

• epsilon (float)

• strict_acyclic (bool)

• disable_structured_sparsity (bool)

• in_flow_edge_type (EdgeType)

• out_flow_edge_type (EdgeType)

• lambda_reg (float)

• force_flow_through_root (bool)

• backend (Backend | None)

Methods

__init__([include_all_terminals, max_flow, ...])
build(graph[, data, prizes, terminals, ...])	Build a single-condition PCST problem from explicit inputs.
build_from_data(graph[, data])	Build the complete optimization problem.
build_many(graph, *, prizes[, terminals, ...])	Build a multi-condition PCST problem from named condition mappings.
create_flow_based_problem(flow_problem, ...)	Create the flow-based optimization problem.
create_problem(graph, data)	Create the optimization problem using a flow-based formulation.
description()	Returns a description of the method.
get_flow_bounds(graph, data)	Get the flow bounds for the optimization problem.
name()	Returns the name of the method.
preprocess(graph, data)	Preprocess the graph and data.
references()	Returns citation keys for this method.
show_bibtex()	Display raw BibTeX entries in a formatted block for easy copying.
show_references()	Display formatted citations in a Jupyter notebook.

Attributes

backend

Return the optimization backend being used.

build(graph, data=None, *, prizes=None, terminals=None, edge_costs=None)

Build a single-condition PCST problem from explicit inputs.

Parameters:

• graph (BaseGraph)

• data (Data | None)

build_many(graph, *, prizes, terminals=None, edge_costs=None)

Build a multi-condition PCST problem from named condition mappings.

Parameters:

graph (BaseGraph)

preprocess(graph, data)

Preprocess the graph and data.

Parameters:

• graph (BaseGraph)

• data (Data)

Return type:

Tuple[BaseGraph, Data]

get_flow_bounds(graph, data)

Get the flow bounds for the optimization problem.

Parameters:

• graph (BaseGraph)

• data (Data)

Return type:

Dict[str, Any]

create_flow_based_problem(flow_problem, graph, data)

Create the flow-based optimization problem.

Parameters:

• flow_problem (ProblemDef)

• graph (BaseGraph)

• data (Data)