# 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
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""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