Código fuente para qiskit_optimization.applications.clique

# This code is part of a Qiskit project.
# (C) Copyright IBM 2018, 2023.
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
# Any modifications or derivative works of this code must retain this
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"""An application class for the clique."""
from typing import Optional, Union, List, Dict

import networkx as nx
import numpy as np
from docplex.mp.model import Model

from qiskit_optimization.algorithms import OptimizationResult
from qiskit_optimization.problems.quadratic_program import QuadraticProgram
from qiskit_optimization.translators import from_docplex_mp
from .graph_optimization_application import GraphOptimizationApplication

[documentos]class Clique(GraphOptimizationApplication): """Optimization application for the "clique" [1] problem based on a NetworkX graph. References: [1]: "Clique (graph theory)", `https://en.wikipedia.org/wiki/Clique_(graph_theory) <https://en.wikipedia.org/wiki/Clique_(graph_theory)>`_ """ def __init__( self, graph: Union[nx.Graph, np.ndarray, List], size: Optional[int] = None ) -> None: """ Args: graph: A graph representing a problem. It can be specified directly as a `NetworkX <https://networkx.org/>`_ graph, or as an array or list format suitable to build out a NetworkX graph. size: The size of the clique. When it's `None`, the default, this class makes an optimization model for a maximal clique instead of the specified size of a clique. """ super().__init__(graph) self._size = size
[documentos] def to_quadratic_program(self) -> QuadraticProgram: """Convert a clique problem instance into a :class:`~qiskit_optimization.problems.QuadraticProgram`. When "size" is None, this makes an optimization model for a maximal clique instead of the specified size of a clique. Returns: The :class:`~qiskit_optimization.problems.QuadraticProgram` created from the clique problem instance. """ complement_g = nx.complement(self._graph) mdl = Model(name="Clique") n = self._graph.number_of_nodes() x = {i: mdl.binary_var(name=f"x_{i}") for i in range(n)} for w, v in complement_g.edges: mdl.add_constraint(x[w] + x[v] <= 1) if self.size is None: mdl.maximize(mdl.sum(x[i] for i in x)) else: mdl.add_constraint(mdl.sum(x[i] for i in x) == self.size) op = from_docplex_mp(mdl) return op
[documentos] def interpret(self, result: Union[OptimizationResult, np.ndarray]) -> List[int]: """Interpret a result as a list of node indices Args: result : The calculated result of the problem Returns: The list of node indices whose corresponding variable is 1 """ x = self._result_to_x(result) clique = [] for i, value in enumerate(x): if value: clique.append(i) return clique
def _draw_result( self, result: Union[OptimizationResult, np.ndarray], pos: Optional[Dict[int, np.ndarray]] = None, ) -> None: """Draw the result with colors Args: result : The calculated result for the problem pos: The positions of nodes """ x = self._result_to_x(result) nx.draw(self._graph, node_color=self._node_colors(x), pos=pos, with_labels=True) def _node_colors(self, x: np.ndarray) -> List[str]: # Return a list of strings for draw. # Color a node with red when the corresponding variable is 1. # Otherwise color it with dark gray. return ["r" if x[node] else "darkgrey" for node in self._graph.nodes] @property def size(self) -> Optional[int]: """Getter of size Returns: The size of the clique, `None` when maximal clique """ return self._size @size.setter def size(self, size: Optional[int]) -> None: """Setter of size Args: size: The size of the clique, `None` for maximal clique """ self._size = size