384 lines
14 KiB
Python
384 lines
14 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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Capacity scaling minimum cost flow algorithm.
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"""
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__author__ = """ysitu <ysitu@users.noreply.github.com>"""
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# Copyright (C) 2014 ysitu <ysitu@users.noreply.github.com>
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# All rights reserved.
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# BSD license.
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__all__ = ['capacity_scaling']
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from itertools import chain
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from math import log
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import networkx as nx
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from ...utils import BinaryHeap
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from ...utils import generate_unique_node
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from ...utils import not_implemented_for
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from ...utils import arbitrary_element
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def _detect_unboundedness(R):
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"""Detect infinite-capacity negative cycles.
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"""
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s = generate_unique_node()
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G = nx.DiGraph()
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G.add_nodes_from(R)
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# Value simulating infinity.
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inf = R.graph['inf']
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# True infinity.
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f_inf = float('inf')
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for u in R:
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for v, e in R[u].items():
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# Compute the minimum weight of infinite-capacity (u, v) edges.
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w = f_inf
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for k, e in e.items():
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if e['capacity'] == inf:
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w = min(w, e['weight'])
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if w != f_inf:
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G.add_edge(u, v, weight=w)
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if nx.negative_edge_cycle(G):
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raise nx.NetworkXUnbounded(
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'Negative cost cycle of infinite capacity found. '
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'Min cost flow may be unbounded below.')
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@not_implemented_for('undirected')
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def _build_residual_network(G, demand, capacity, weight):
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"""Build a residual network and initialize a zero flow.
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"""
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if sum(G.nodes[u].get(demand, 0) for u in G) != 0:
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raise nx.NetworkXUnfeasible("Sum of the demands should be 0.")
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R = nx.MultiDiGraph()
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R.add_nodes_from((u, {'excess': -G.nodes[u].get(demand, 0),
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'potential': 0}) for u in G)
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inf = float('inf')
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# Detect selfloops with infinite capacities and negative weights.
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for u, v, e in nx.selfloop_edges(G, data=True):
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if e.get(weight, 0) < 0 and e.get(capacity, inf) == inf:
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raise nx.NetworkXUnbounded(
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'Negative cost cycle of infinite capacity found. '
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'Min cost flow may be unbounded below.')
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# Extract edges with positive capacities. Self loops excluded.
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if G.is_multigraph():
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edge_list = [(u, v, k, e)
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for u, v, k, e in G.edges(data=True, keys=True)
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if u != v and e.get(capacity, inf) > 0]
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else:
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edge_list = [(u, v, 0, e) for u, v, e in G.edges(data=True)
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if u != v and e.get(capacity, inf) > 0]
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# Simulate infinity with the larger of the sum of absolute node imbalances
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# the sum of finite edge capacities or any positive value if both sums are
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# zero. This allows the infinite-capacity edges to be distinguished for
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# unboundedness detection and directly participate in residual capacity
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# calculation.
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inf = max(sum(abs(R.nodes[u]['excess']) for u in R),
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2 * sum(e[capacity] for u, v, k, e in edge_list
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if capacity in e and e[capacity] != inf)) or 1
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for u, v, k, e in edge_list:
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r = min(e.get(capacity, inf), inf)
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w = e.get(weight, 0)
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# Add both (u, v) and (v, u) into the residual network marked with the
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# original key. (key[1] == True) indicates the (u, v) is in the
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# original network.
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R.add_edge(u, v, key=(k, True), capacity=r, weight=w, flow=0)
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R.add_edge(v, u, key=(k, False), capacity=0, weight=-w, flow=0)
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# Record the value simulating infinity.
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R.graph['inf'] = inf
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_detect_unboundedness(R)
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return R
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def _build_flow_dict(G, R, capacity, weight):
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"""Build a flow dictionary from a residual network.
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"""
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inf = float('inf')
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flow_dict = {}
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if G.is_multigraph():
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for u in G:
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flow_dict[u] = {}
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for v, es in G[u].items():
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flow_dict[u][v] = dict(
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# Always saturate negative selfloops.
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(k, (0 if (u != v or e.get(capacity, inf) <= 0 or
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e.get(weight, 0) >= 0) else e[capacity]))
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for k, e in es.items())
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for v, es in R[u].items():
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if v in flow_dict[u]:
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flow_dict[u][v].update((k[0], e['flow'])
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for k, e in es.items()
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if e['flow'] > 0)
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else:
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for u in G:
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flow_dict[u] = dict(
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# Always saturate negative selfloops.
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(v, (0 if (u != v or e.get(capacity, inf) <= 0 or
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e.get(weight, 0) >= 0) else e[capacity]))
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for v, e in G[u].items())
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flow_dict[u].update((v, e['flow']) for v, es in R[u].items()
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for e in es.values() if e['flow'] > 0)
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return flow_dict
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def capacity_scaling(G, demand='demand', capacity='capacity', weight='weight',
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heap=BinaryHeap):
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r"""Find a minimum cost flow satisfying all demands in digraph G.
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This is a capacity scaling successive shortest augmenting path algorithm.
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G is a digraph with edge costs and capacities and in which nodes
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have demand, i.e., they want to send or receive some amount of
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flow. A negative demand means that the node wants to send flow, a
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positive demand means that the node want to receive flow. A flow on
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the digraph G satisfies all demand if the net flow into each node
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is equal to the demand of that node.
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Parameters
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----------
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G : NetworkX graph
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DiGraph or MultiDiGraph on which a minimum cost flow satisfying all
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demands is to be found.
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demand : string
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Nodes of the graph G are expected to have an attribute demand
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that indicates how much flow a node wants to send (negative
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demand) or receive (positive demand). Note that the sum of the
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demands should be 0 otherwise the problem in not feasible. If
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this attribute is not present, a node is considered to have 0
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demand. Default value: 'demand'.
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capacity : string
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Edges of the graph G are expected to have an attribute capacity
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that indicates how much flow the edge can support. If this
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attribute is not present, the edge is considered to have
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infinite capacity. Default value: 'capacity'.
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weight : string
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Edges of the graph G are expected to have an attribute weight
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that indicates the cost incurred by sending one unit of flow on
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that edge. If not present, the weight is considered to be 0.
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Default value: 'weight'.
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heap : class
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Type of heap to be used in the algorithm. It should be a subclass of
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:class:`MinHeap` or implement a compatible interface.
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If a stock heap implementation is to be used, :class:`BinaryHeap` is
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recommended over :class:`PairingHeap` for Python implementations without
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optimized attribute accesses (e.g., CPython) despite a slower
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asymptotic running time. For Python implementations with optimized
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attribute accesses (e.g., PyPy), :class:`PairingHeap` provides better
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performance. Default value: :class:`BinaryHeap`.
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Returns
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-------
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flowCost : integer
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Cost of a minimum cost flow satisfying all demands.
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flowDict : dictionary
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If G is a digraph, a dict-of-dicts keyed by nodes such that
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flowDict[u][v] is the flow on edge (u, v).
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If G is a MultiDiGraph, a dict-of-dicts-of-dicts keyed by nodes
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so that flowDict[u][v][key] is the flow on edge (u, v, key).
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Raises
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------
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NetworkXError
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This exception is raised if the input graph is not directed,
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not connected.
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NetworkXUnfeasible
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This exception is raised in the following situations:
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* The sum of the demands is not zero. Then, there is no
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flow satisfying all demands.
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* There is no flow satisfying all demand.
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NetworkXUnbounded
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This exception is raised if the digraph G has a cycle of
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negative cost and infinite capacity. Then, the cost of a flow
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satisfying all demands is unbounded below.
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Notes
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-----
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This algorithm does not work if edge weights are floating-point numbers.
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See also
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--------
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:meth:`network_simplex`
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Examples
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--------
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A simple example of a min cost flow problem.
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>>> import networkx as nx
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>>> G = nx.DiGraph()
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>>> G.add_node('a', demand = -5)
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>>> G.add_node('d', demand = 5)
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>>> G.add_edge('a', 'b', weight = 3, capacity = 4)
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>>> G.add_edge('a', 'c', weight = 6, capacity = 10)
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>>> G.add_edge('b', 'd', weight = 1, capacity = 9)
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>>> G.add_edge('c', 'd', weight = 2, capacity = 5)
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>>> flowCost, flowDict = nx.capacity_scaling(G)
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>>> flowCost
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24
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>>> flowDict # doctest: +SKIP
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{'a': {'c': 1, 'b': 4}, 'c': {'d': 1}, 'b': {'d': 4}, 'd': {}}
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It is possible to change the name of the attributes used for the
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algorithm.
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>>> G = nx.DiGraph()
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>>> G.add_node('p', spam = -4)
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>>> G.add_node('q', spam = 2)
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>>> G.add_node('a', spam = -2)
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>>> G.add_node('d', spam = -1)
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>>> G.add_node('t', spam = 2)
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>>> G.add_node('w', spam = 3)
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>>> G.add_edge('p', 'q', cost = 7, vacancies = 5)
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>>> G.add_edge('p', 'a', cost = 1, vacancies = 4)
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>>> G.add_edge('q', 'd', cost = 2, vacancies = 3)
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>>> G.add_edge('t', 'q', cost = 1, vacancies = 2)
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>>> G.add_edge('a', 't', cost = 2, vacancies = 4)
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>>> G.add_edge('d', 'w', cost = 3, vacancies = 4)
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>>> G.add_edge('t', 'w', cost = 4, vacancies = 1)
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>>> flowCost, flowDict = nx.capacity_scaling(G, demand = 'spam',
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... capacity = 'vacancies',
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... weight = 'cost')
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>>> flowCost
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37
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>>> flowDict # doctest: +SKIP
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{'a': {'t': 4}, 'd': {'w': 2}, 'q': {'d': 1}, 'p': {'q': 2, 'a': 2}, 't': {'q': 1, 'w': 1}, 'w': {}}
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"""
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R = _build_residual_network(G, demand, capacity, weight)
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inf = float('inf')
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# Account cost of negative selfloops.
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flow_cost = sum(
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0 if e.get(capacity, inf) <= 0 or e.get(weight, 0) >= 0
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else e[capacity] * e[weight]
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for u, v, e in nx.selfloop_edges(G, data=True))
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# Determine the maxmimum edge capacity.
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wmax = max(chain([-inf],
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(e['capacity'] for u, v, e in R.edges(data=True))))
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if wmax == -inf:
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# Residual network has no edges.
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return flow_cost, _build_flow_dict(G, R, capacity, weight)
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R_nodes = R.nodes
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R_succ = R.succ
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delta = 2 ** int(log(wmax, 2))
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while delta >= 1:
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# Saturate Δ-residual edges with negative reduced costs to achieve
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# Δ-optimality.
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for u in R:
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p_u = R_nodes[u]['potential']
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for v, es in R_succ[u].items():
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for k, e in es.items():
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flow = e['capacity'] - e['flow']
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if e['weight'] - p_u + R_nodes[v]['potential'] < 0:
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flow = e['capacity'] - e['flow']
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if flow >= delta:
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e['flow'] += flow
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R_succ[v][u][(k[0], not k[1])]['flow'] -= flow
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R_nodes[u]['excess'] -= flow
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R_nodes[v]['excess'] += flow
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# Determine the Δ-active nodes.
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S = set()
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T = set()
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S_add = S.add
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S_remove = S.remove
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T_add = T.add
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T_remove = T.remove
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for u in R:
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excess = R_nodes[u]['excess']
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if excess >= delta:
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S_add(u)
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elif excess <= -delta:
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T_add(u)
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# Repeatedly augment flow from S to T along shortest paths until
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# Δ-feasibility is achieved.
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while S and T:
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s = arbitrary_element(S)
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t = None
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# Search for a shortest path in terms of reduce costs from s to
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# any t in T in the Δ-residual network.
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d = {}
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pred = {s: None}
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h = heap()
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h_insert = h.insert
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h_get = h.get
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h_insert(s, 0)
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while h:
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u, d_u = h.pop()
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d[u] = d_u
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if u in T:
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# Path found.
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t = u
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break
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p_u = R_nodes[u]['potential']
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for v, es in R_succ[u].items():
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if v in d:
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continue
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wmin = inf
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# Find the minimum-weighted (u, v) Δ-residual edge.
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for k, e in es.items():
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if e['capacity'] - e['flow'] >= delta:
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w = e['weight']
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if w < wmin:
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wmin = w
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kmin = k
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emin = e
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if wmin == inf:
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continue
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# Update the distance label of v.
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d_v = d_u + wmin - p_u + R_nodes[v]['potential']
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if h_insert(v, d_v):
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pred[v] = (u, kmin, emin)
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if t is not None:
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# Augment Δ units of flow from s to t.
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while u != s:
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v = u
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u, k, e = pred[v]
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e['flow'] += delta
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R_succ[v][u][(k[0], not k[1])]['flow'] -= delta
|
||
|
|
# Account node excess and deficit.
|
||
|
|
R_nodes[s]['excess'] -= delta
|
||
|
|
R_nodes[t]['excess'] += delta
|
||
|
|
if R_nodes[s]['excess'] < delta:
|
||
|
|
S_remove(s)
|
||
|
|
if R_nodes[t]['excess'] > -delta:
|
||
|
|
T_remove(t)
|
||
|
|
# Update node potentials.
|
||
|
|
d_t = d[t]
|
||
|
|
for u, d_u in d.items():
|
||
|
|
R_nodes[u]['potential'] -= d_u - d_t
|
||
|
|
else:
|
||
|
|
# Path not found.
|
||
|
|
S_remove(s)
|
||
|
|
delta //= 2
|
||
|
|
|
||
|
|
if any(R.nodes[u]['excess'] != 0 for u in R):
|
||
|
|
raise nx.NetworkXUnfeasible('No flow satisfying all demands.')
|
||
|
|
|
||
|
|
# Calculate the flow cost.
|
||
|
|
for u in R:
|
||
|
|
for v, es in R_succ[u].items():
|
||
|
|
for e in es.values():
|
||
|
|
flow = e['flow']
|
||
|
|
if flow > 0:
|
||
|
|
flow_cost += flow * e['weight']
|
||
|
|
|
||
|
|
return flow_cost, _build_flow_dict(G, R, capacity, weight)
|