corneto.methods.SteinerTreeFlow

class corneto.methods.SteinerTreeFlow(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=True, backend=None)

Bases: PrizeCollectingSteinerTree

Basic Steiner Tree optimization method as a flow-based problem.

Given a graph and a set of terminal nodes, this method finds a minimal-weight connected subgraph (tree) that spans all terminals.

Parameters:

• 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__(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=True, backend=None)

Parameters:

• 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__([max_flow, default_edge_cost, ...])
build(graph[, data, terminals, edge_costs])	Build a single-condition Steiner tree from explicit inputs.
build_from_data(graph[, data])	Build the complete optimization problem.
build_many(graph, *, terminals[, edge_costs])	Build multiple named Steiner tree conditions.
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, *, terminals=None, edge_costs=None)

Build a single-condition Steiner tree from explicit inputs.

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

Build multiple named Steiner tree conditions.