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