250 lines
7.8 KiB
Python
250 lines
7.8 KiB
Python
# -*- coding: utf-8 -*-
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"""Functions for computing treewidth decomposition.
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Treewidth of an undirected graph is a number associated with the graph.
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It can be defined as the size of the largest vertex set (bag) in a tree
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decomposition of the graph minus one.
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`Wikipedia: Treewidth <https://en.wikipedia.org/wiki/Treewidth>`_
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The notions of treewidth and tree decomposition have gained their
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attractiveness partly because many graph and network problems that are
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intractable (e.g., NP-hard) on arbitrary graphs become efficiently
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solvable (e.g., with a linear time algorithm) when the treewidth of the
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input graphs is bounded by a constant [1]_ [2]_.
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There are two different functions for computing a tree decomposition:
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:func:`treewidth_min_degree` and :func:`treewidth_min_fill_in`.
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.. [1] Hans L. Bodlaender and Arie M. C. A. Koster. 2010. "Treewidth
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computations I.Upper bounds". Inf. Comput. 208, 3 (March 2010),259-275.
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http://dx.doi.org/10.1016/j.ic.2009.03.008
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.. [2] Hand L. Bodlaender. "Discovering Treewidth". Institute of Information
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and Computing Sciences, Utrecht University.
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Technical Report UU-CS-2005-018.
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http://www.cs.uu.nl
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.. [3] K. Wang, Z. Lu, and J. Hicks *Treewidth*.
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http://web.eecs.utk.edu/~cphillip/cs594_spring2015_projects/treewidth.pdf
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"""
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import sys
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import networkx as nx
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from networkx.utils import not_implemented_for
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from heapq import heappush, heappop, heapify
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import itertools
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__all__ = ["treewidth_min_degree", "treewidth_min_fill_in"]
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@not_implemented_for('directed')
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@not_implemented_for('multigraph')
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def treewidth_min_degree(G):
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""" Returns a treewidth decomposition using the Minimum Degree heuristic.
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The heuristic chooses the nodes according to their degree, i.e., first
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the node with the lowest degree is chosen, then the graph is updated
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and the corresponding node is removed. Next, a new node with the lowest
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degree is chosen, and so on.
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Parameters
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----------
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G : NetworkX graph
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Returns
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-------
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Treewidth decomposition : (int, Graph) tuple
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2-tuple with treewidth and the corresponding decomposed tree.
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"""
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deg_heuristic = MinDegreeHeuristic(G)
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return treewidth_decomp(G, lambda graph: deg_heuristic.best_node(graph))
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@not_implemented_for('directed')
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@not_implemented_for('multigraph')
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def treewidth_min_fill_in(G):
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""" Returns a treewidth decomposition using the Minimum Fill-in heuristic.
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The heuristic chooses a node from the graph, where the number of edges
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added turning the neighbourhood of the chosen node into clique is as
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small as possible.
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Parameters
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----------
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G : NetworkX graph
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Returns
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-------
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Treewidth decomposition : (int, Graph) tuple
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2-tuple with treewidth and the corresponding decomposed tree.
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"""
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return treewidth_decomp(G, min_fill_in_heuristic)
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class MinDegreeHeuristic:
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""" Implements the Minimum Degree heuristic.
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The heuristic chooses the nodes according to their degree
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(number of neighbours), i.e., first the node with the lowest degree is
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chosen, then the graph is updated and the corresponding node is
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removed. Next, a new node with the lowest degree is chosen, and so on.
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"""
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def __init__(self, graph):
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self._graph = graph
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# nodes that have to be updated in the heap before each iteration
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self._update_nodes = []
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self._degreeq = [] # a heapq with 2-tuples (degree,node)
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# build heap with initial degrees
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for n in graph:
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self._degreeq.append((len(graph[n]), n))
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heapify(self._degreeq)
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def best_node(self, graph):
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# update nodes in self._update_nodes
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for n in self._update_nodes:
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# insert changed degrees into degreeq
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heappush(self._degreeq, (len(graph[n]), n))
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# get the next valid (minimum degree) node
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while self._degreeq:
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(min_degree, elim_node) = heappop(self._degreeq)
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if elim_node not in graph or len(graph[elim_node]) != min_degree:
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# outdated entry in degreeq
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continue
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elif min_degree == len(graph) - 1:
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# fully connected: abort condition
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return None
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# remember to update nodes in the heap before getting the next node
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self._update_nodes = graph[elim_node]
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return elim_node
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# the heap is empty: abort
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return None
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def min_fill_in_heuristic(graph):
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""" Implements the Minimum Degree heuristic.
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Returns the node from the graph, where the number of edges added when
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turning the neighbourhood of the chosen node into clique is as small as
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possible. This algorithm chooses the nodes using the Minimum Fill-In
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heuristic. The running time of the algorithm is :math:`O(V^3)` and it uses
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additional constant memory."""
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if len(graph) == 0:
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return None
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min_fill_in_node = None
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min_fill_in = sys.maxsize
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# create sorted list of (degree, node)
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degree_list = [(len(graph[node]), node) for node in graph]
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degree_list.sort()
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# abort condition
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min_degree = degree_list[0][0]
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if min_degree == len(graph) - 1:
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return None
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for (_, node) in degree_list:
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num_fill_in = 0
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nbrs = graph[node]
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for nbr in nbrs:
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# count how many nodes in nbrs current nbr is not connected to
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# subtract 1 for the node itself
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num_fill_in += len(nbrs - graph[nbr]) - 1
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if num_fill_in >= 2 * min_fill_in:
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break
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num_fill_in /= 2 # divide by 2 because of double counting
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if num_fill_in < min_fill_in: # update min-fill-in node
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if num_fill_in == 0:
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return node
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min_fill_in = num_fill_in
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min_fill_in_node = node
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return min_fill_in_node
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def treewidth_decomp(G, heuristic=min_fill_in_heuristic):
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""" Returns a treewidth decomposition using the passed heuristic.
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Parameters
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----------
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G : NetworkX graph
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heuristic : heuristic function
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Returns
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-------
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Treewidth decomposition : (int, Graph) tuple
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2-tuple with treewidth and the corresponding decomposed tree.
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"""
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# make dict-of-sets structure
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graph = {n: set(G[n]) - set([n]) for n in G}
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# stack containing nodes and neighbors in the order from the heuristic
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node_stack = []
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# get first node from heuristic
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elim_node = heuristic(graph)
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while elim_node is not None:
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# connect all neighbours with each other
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nbrs = graph[elim_node]
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for u, v in itertools.permutations(nbrs, 2):
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if v not in graph[u]:
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graph[u].add(v)
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# push node and its current neighbors on stack
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node_stack.append((elim_node, nbrs))
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# remove node from graph
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for u in graph[elim_node]:
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graph[u].remove(elim_node)
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del graph[elim_node]
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elim_node = heuristic(graph)
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# the abort condition is met; put all remaining nodes into one bag
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decomp = nx.Graph()
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first_bag = frozenset(graph.keys())
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decomp.add_node(first_bag)
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treewidth = len(first_bag) - 1
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while node_stack:
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# get node and its neighbors from the stack
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(curr_node, nbrs) = node_stack.pop()
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# find a bag all neighbors are in
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old_bag = None
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for bag in decomp.nodes:
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if nbrs <= bag:
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old_bag = bag
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break
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if old_bag is None:
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# no old_bag was found: just connect to the first_bag
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old_bag = first_bag
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# create new node for decomposition
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nbrs.add(curr_node)
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new_bag = frozenset(nbrs)
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# update treewidth
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treewidth = max(treewidth, len(new_bag) - 1)
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# add edge to decomposition (implicitly also adds the new node)
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decomp.add_edge(old_bag, new_bag)
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return treewidth, decomp
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