# Source code for networkx.algorithms.flow.networksimplex

```"""
Minimum cost flow algorithms on directed connected graphs.
"""

__all__ = ["network_simplex"]

from itertools import chain, islice, repeat
from math import ceil, sqrt

import networkx as nx
from networkx.utils import not_implemented_for

class _DataEssentialsAndFunctions:
def __init__(
self, G, multigraph, demand="demand", capacity="capacity", weight="weight"
):

# Number all nodes and edges and hereafter reference them using ONLY their numbers
self.node_list = list(G)  # nodes
self.node_indices = {u: i for i, u in enumerate(self.node_list)}  # node indices
self.node_demands = [
G.nodes[u].get(demand, 0) for u in self.node_list
]  # node demands

self.edge_sources = []  # edge sources
self.edge_targets = []  # edge targets
if multigraph:
self.edge_keys = []  # edge keys
self.edge_indices = {}  # edge indices
self.edge_capacities = []  # edge capacities
self.edge_weights = []  # edge weights

if not multigraph:
edges = G.edges(data=True)
else:
edges = G.edges(data=True, keys=True)

inf = float("inf")
edges = (e for e in edges if e != e and e[-1].get(capacity, inf) != 0)
for i, e in enumerate(edges):
self.edge_sources.append(self.node_indices[e])
self.edge_targets.append(self.node_indices[e])
if multigraph:
self.edge_keys.append(e)
self.edge_indices[e[:-1]] = i
self.edge_capacities.append(e[-1].get(capacity, inf))
self.edge_weights.append(e[-1].get(weight, 0))

# spanning tree specific data to be initialized

self.edge_count = None  # number of edges
self.edge_flow = None  # edge flows
self.node_potentials = None  # node potentials
self.parent = None  # parent nodes
self.parent_edge = None  # edges to parents
self.subtree_size = None  # subtree sizes
self.next_node_dft = None  # next nodes in depth-first thread
self.prev_node_dft = None  # previous nodes in depth-first thread
self.last_descendent_dft = None  # last descendants in depth-first thread
self._spanning_tree_initialized = (
False  # False until initialize_spanning_tree() is called
)

def initialize_spanning_tree(self, n, faux_inf):
self.edge_count = len(self.edge_indices)  # number of edges
self.edge_flow = list(
chain(repeat(0, self.edge_count), (abs(d) for d in self.node_demands))
)  # edge flows
self.node_potentials = [
faux_inf if d <= 0 else -faux_inf for d in self.node_demands
]  # node potentials
self.parent = list(chain(repeat(-1, n), [None]))  # parent nodes
self.parent_edge = list(
range(self.edge_count, self.edge_count + n)
)  # edges to parents
self.subtree_size = list(chain(repeat(1, n), [n + 1]))  # subtree sizes
self.next_node_dft = list(
chain(range(1, n), [-1, 0])
)  # next nodes in depth-first thread
self.prev_node_dft = list(range(-1, n))  # previous nodes in depth-first thread
self.last_descendent_dft = list(
chain(range(n), [n - 1])
)  # last descendants in depth-first thread
self._spanning_tree_initialized = True  # True only if all the assignments pass

def find_apex(self, p, q):
"""
Find the lowest common ancestor of nodes p and q in the spanning tree.
"""
size_p = self.subtree_size[p]
size_q = self.subtree_size[q]
while True:
while size_p < size_q:
p = self.parent[p]
size_p = self.subtree_size[p]
while size_p > size_q:
q = self.parent[q]
size_q = self.subtree_size[q]
if size_p == size_q:
if p != q:
p = self.parent[p]
size_p = self.subtree_size[p]
q = self.parent[q]
size_q = self.subtree_size[q]
else:
return p

def trace_path(self, p, w):
"""
Returns the nodes and edges on the path from node p to its ancestor w.
"""
Wn = [p]
We = []
while p != w:
We.append(self.parent_edge[p])
p = self.parent[p]
Wn.append(p)
return Wn, We

def find_cycle(self, i, p, q):
"""
Returns the nodes and edges on the cycle containing edge i == (p, q)
when the latter is added to the spanning tree.

The cycle is oriented in the direction from p to q.
"""
w = self.find_apex(p, q)
Wn, We = self.trace_path(p, w)
Wn.reverse()
We.reverse()
if We != [i]:
We.append(i)
WnR, WeR = self.trace_path(q, w)
del WnR[-1]
Wn += WnR
We += WeR
return Wn, We

def augment_flow(self, Wn, We, f):
"""
Augment f units of flow along a cycle represented by Wn and We.
"""
for i, p in zip(We, Wn):
if self.edge_sources[i] == p:
self.edge_flow[i] += f
else:
self.edge_flow[i] -= f

def trace_subtree(self, p):
"""
Yield the nodes in the subtree rooted at a node p.
"""
yield p
l = self.last_descendent_dft[p]
while p != l:
p = self.next_node_dft[p]
yield p

def remove_edge(self, s, t):
"""
Remove an edge (s, t) where parent[t] == s from the spanning tree.
"""
size_t = self.subtree_size[t]
prev_t = self.prev_node_dft[t]
last_t = self.last_descendent_dft[t]
next_last_t = self.next_node_dft[last_t]
# Remove (s, t).
self.parent[t] = None
self.parent_edge[t] = None
# Remove the subtree rooted at t from the depth-first thread.
self.next_node_dft[prev_t] = next_last_t
self.prev_node_dft[next_last_t] = prev_t
self.next_node_dft[last_t] = t
self.prev_node_dft[t] = last_t
# Update the subtree sizes and last descendants of the (old) acenstors
# of t.
while s is not None:
self.subtree_size[s] -= size_t
if self.last_descendent_dft[s] == last_t:
self.last_descendent_dft[s] = prev_t
s = self.parent[s]

def make_root(self, q):
"""
Make a node q the root of its containing subtree.
"""
ancestors = []
while q is not None:
ancestors.append(q)
q = self.parent[q]
ancestors.reverse()
for p, q in zip(ancestors, islice(ancestors, 1, None)):
size_p = self.subtree_size[p]
last_p = self.last_descendent_dft[p]
prev_q = self.prev_node_dft[q]
last_q = self.last_descendent_dft[q]
next_last_q = self.next_node_dft[last_q]
# Make p a child of q.
self.parent[p] = q
self.parent[q] = None
self.parent_edge[p] = self.parent_edge[q]
self.parent_edge[q] = None
self.subtree_size[p] = size_p - self.subtree_size[q]
self.subtree_size[q] = size_p
# Remove the subtree rooted at q from the depth-first thread.
self.next_node_dft[prev_q] = next_last_q
self.prev_node_dft[next_last_q] = prev_q
self.next_node_dft[last_q] = q
self.prev_node_dft[q] = last_q
if last_p == last_q:
self.last_descendent_dft[p] = prev_q
last_p = prev_q
# Add the remaining parts of the subtree rooted at p as a subtree
# of q in the depth-first thread.
self.prev_node_dft[p] = last_q
self.next_node_dft[last_q] = p
self.next_node_dft[last_p] = q
self.prev_node_dft[q] = last_p
self.last_descendent_dft[q] = last_p

"""
Add an edge (p, q) to the spanning tree where q is the root of a subtree.
"""
last_p = self.last_descendent_dft[p]
next_last_p = self.next_node_dft[last_p]
size_q = self.subtree_size[q]
last_q = self.last_descendent_dft[q]
# Make q a child of p.
self.parent[q] = p
self.parent_edge[q] = i
# Insert the subtree rooted at q into the depth-first thread.
self.next_node_dft[last_p] = q
self.prev_node_dft[q] = last_p
self.prev_node_dft[next_last_p] = last_q
self.next_node_dft[last_q] = next_last_p
# Update the subtree sizes and last descendants of the (new) ancestors
# of q.
while p is not None:
self.subtree_size[p] += size_q
if self.last_descendent_dft[p] == last_p:
self.last_descendent_dft[p] = last_q
p = self.parent[p]

def update_potentials(self, i, p, q):
"""
Update the potentials of the nodes in the subtree rooted at a node
q connected to its parent p by an edge i.
"""
if q == self.edge_targets[i]:
d = self.node_potentials[p] - self.edge_weights[i] - self.node_potentials[q]
else:
d = self.node_potentials[p] + self.edge_weights[i] - self.node_potentials[q]
for q in self.trace_subtree(q):
self.node_potentials[q] += d

def reduced_cost(self, i):
"""Returns the reduced cost of an edge i."""
c = (
self.edge_weights[i]
- self.node_potentials[self.edge_sources[i]]
+ self.node_potentials[self.edge_targets[i]]
)
return c if self.edge_flow[i] == 0 else -c

def find_entering_edges(self):
"""Yield entering edges until none can be found."""
if self.edge_count == 0:
return

# Entering edges are found by combining Dantzig's rule and Bland's
# rule. The edges are cyclically grouped into blocks of size B. Within
# each block, Dantzig's rule is applied to find an entering edge. The
# blocks to search is determined following Bland's rule.
B = int(ceil(sqrt(self.edge_count)))  # pivot block size
M = (self.edge_count + B - 1) // B  # number of blocks needed to cover all edges
m = 0  # number of consecutive blocks without eligible
# entering edges
f = 0  # first edge in block
while m < M:
# Determine the next block of edges.
l = f + B
if l <= self.edge_count:
edges = range(f, l)
else:
l -= self.edge_count
edges = chain(range(f, self.edge_count), range(l))
f = l
# Find the first edge with the lowest reduced cost.
i = min(edges, key=self.reduced_cost)
c = self.reduced_cost(i)
if c >= 0:
# No entering edge found in the current block.
m += 1
else:
# Entering edge found.
if self.edge_flow[i] == 0:
p = self.edge_sources[i]
q = self.edge_targets[i]
else:
p = self.edge_targets[i]
q = self.edge_sources[i]
yield i, p, q
m = 0
# All edges have nonnegative reduced costs. The current flow is
# optimal.

def residual_capacity(self, i, p):
"""Returns the residual capacity of an edge i in the direction away
from its endpoint p.
"""
return (
self.edge_capacities[i] - self.edge_flow[i]
if self.edge_sources[i] == p
else self.edge_flow[i]
)

def find_leaving_edge(self, Wn, We):
"""Returns the leaving edge in a cycle represented by Wn and We."""
j, s = min(
zip(reversed(We), reversed(Wn)),
key=lambda i_p: self.residual_capacity(*i_p),
)
t = self.edge_targets[j] if self.edge_sources[j] == s else self.edge_sources[j]
return j, s, t

[docs]@not_implemented_for("undirected")
def network_simplex(G, demand="demand", capacity="capacity", weight="weight"):
r"""Find a minimum cost flow satisfying all demands in digraph G.

This is a primal network simplex algorithm that uses the leaving
arc rule to prevent cycling.

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 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'.

Returns
-------
flowCost : integer, float
Cost of a minimum cost flow satisfying all demands.

flowDict : dictionary
Dictionary of dictionaries keyed by nodes such that
flowDict[u][v] is the flow edge (u, v).

Raises
------
NetworkXError
This exception is raised if the input graph is not directed or
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 is not guaranteed to work if edge weights or demands
are floating point numbers (overflows and roundoff errors can
cause problems). As a workaround you can use integer numbers by
multiplying the relevant edge attributes by a convenient
constant factor (eg 100).

--------
cost_of_flow, max_flow_min_cost, min_cost_flow, min_cost_flow_cost

Examples
--------
A simple example of a min cost flow problem.

>>> G = nx.DiGraph()
>>> flowCost, flowDict = nx.network_simplex(G)
>>> flowCost
24
>>> flowDict
{'a': {'b': 4, 'c': 1}, 'd': {}, 'b': {'d': 4}, 'c': {'d': 1}}

The mincost flow algorithm can also be used to solve shortest path
problems. To find the shortest path between two nodes u and v,
give all edges an infinite capacity, give node u a demand of -1 and
node v a demand a 1. Then run the network simplex. The value of a
min cost flow will be the distance between u and v and edges
carrying positive flow will indicate the path.

>>> G = nx.DiGraph()
...     [
...         ("s", "u", 10),
...         ("s", "x", 5),
...         ("u", "v", 1),
...         ("u", "x", 2),
...         ("v", "y", 1),
...         ("x", "u", 3),
...         ("x", "v", 5),
...         ("x", "y", 2),
...         ("y", "s", 7),
...         ("y", "v", 6),
...     ]
... )
>>> flowCost, flowDict = nx.network_simplex(G)
>>> flowCost == nx.shortest_path_length(G, "s", "v", weight="weight")
True
>>> sorted([(u, v) for u in flowDict for v in flowDict[u] if flowDict[u][v] > 0])
[('s', 'x'), ('u', 'v'), ('x', 'u')]
>>> nx.shortest_path(G, "s", "v", weight="weight")
['s', 'x', 'u', 'v']

It is possible to change the name of the attributes used for the
algorithm.

>>> G = nx.DiGraph()
>>> flowCost, flowDict = nx.network_simplex(
...     G, demand="spam", capacity="vacancies", weight="cost"
... )
>>> flowCost
37
>>> flowDict
{'p': {'q': 2, 'a': 2}, 'q': {'d': 1}, 'a': {'t': 4}, 'd': {'w': 2}, 't': {'q': 1, 'w': 1}, 'w': {}}

References
----------
..  Z. Kiraly, P. Kovacs.
Efficient implementation of minimum-cost flow algorithms.
Acta Universitatis Sapientiae, Informatica 4(1):67--118. 2012.
..  R. Barr, F. Glover, D. Klingman.
Enhancement of spanning tree labeling procedures for network
optimization.
INFOR 17(1):16--34. 1979.
"""
###########################################################################
# Problem essentials extraction and sanity check
###########################################################################

if len(G) == 0:
raise nx.NetworkXError("graph has no nodes")

multigraph = G.is_multigraph()

# extracting data essential to problem
DEAF = _DataEssentialsAndFunctions(
G, multigraph, demand=demand, capacity=capacity, weight=weight
)

###########################################################################
# Quick Error Detection
###########################################################################

inf = float("inf")
for u, d in zip(DEAF.node_list, DEAF.node_demands):
if abs(d) == inf:
raise nx.NetworkXError(f"node {u!r} has infinite demand")
for e, w in zip(DEAF.edge_indices, DEAF.edge_weights):
if abs(w) == inf:
raise nx.NetworkXError(f"edge {e!r} has infinite weight")
if not multigraph:
edges = nx.selfloop_edges(G, data=True)
else:
edges = nx.selfloop_edges(G, data=True, keys=True)
for e in edges:
if abs(e[-1].get(weight, 0)) == inf:
raise nx.NetworkXError(f"edge {e[:-1]!r} has infinite weight")

###########################################################################
# Quick Infeasibility Detection
###########################################################################

if sum(DEAF.node_demands) != 0:
raise nx.NetworkXUnfeasible("total node demand is not zero")
for e, c in zip(DEAF.edge_indices, DEAF.edge_capacities):
if c < 0:
raise nx.NetworkXUnfeasible(f"edge {e!r} has negative capacity")
if not multigraph:
edges = nx.selfloop_edges(G, data=True)
else:
edges = nx.selfloop_edges(G, data=True, keys=True)
for e in edges:
if e[-1].get(capacity, inf) < 0:
raise nx.NetworkXUnfeasible(f"edge {e[:-1]!r} has negative capacity")

###########################################################################
# Initialization
###########################################################################

# Add a dummy node -1 and connect all existing nodes to it with infinite-
# capacity dummy edges. Node -1 will serve as the root of the
# spanning tree of the network simplex method. The new edges will used to
# trivially satisfy the node demands and create an initial strongly
# feasible spanning tree.
for i, d in enumerate(DEAF.node_demands):
# Must be greater-than here. Zero-demand nodes must have
# edges pointing towards the root to ensure strong feasibility.
if d > 0:
DEAF.edge_sources.append(-1)
DEAF.edge_targets.append(i)
else:
DEAF.edge_sources.append(i)
DEAF.edge_targets.append(-1)
faux_inf = (
3
* max(
chain(
[
sum(c for c in DEAF.edge_capacities if c < inf),
sum(abs(w) for w in DEAF.edge_weights),
],
(abs(d) for d in DEAF.node_demands),
)
)
or 1
)

n = len(DEAF.node_list)  # number of nodes
DEAF.edge_weights.extend(repeat(faux_inf, n))
DEAF.edge_capacities.extend(repeat(faux_inf, n))

# Construct the initial spanning tree.
DEAF.initialize_spanning_tree(n, faux_inf)

###########################################################################
# Pivot loop
###########################################################################

for i, p, q in DEAF.find_entering_edges():
Wn, We = DEAF.find_cycle(i, p, q)
j, s, t = DEAF.find_leaving_edge(Wn, We)
DEAF.augment_flow(Wn, We, DEAF.residual_capacity(j, s))
# Do nothing more if the entering edge is the same as the leaving edge.
if i != j:
if DEAF.parent[t] != s:
# Ensure that s is the parent of t.
s, t = t, s
if We.index(i) > We.index(j):
# Ensure that q is in the subtree rooted at t.
p, q = q, p
DEAF.remove_edge(s, t)
DEAF.make_root(q)
DEAF.update_potentials(i, p, q)

###########################################################################
# Infeasibility and unboundedness detection
###########################################################################

if any(DEAF.edge_flow[i] != 0 for i in range(-n, 0)):
raise nx.NetworkXUnfeasible("no flow satisfies all node demands")

if any(DEAF.edge_flow[i] * 2 >= faux_inf for i in range(DEAF.edge_count)) or any(
e[-1].get(capacity, inf) == inf and e[-1].get(weight, 0) < 0
for e in nx.selfloop_edges(G, data=True)
):
raise nx.NetworkXUnbounded("negative cycle with infinite capacity found")

###########################################################################
# Flow cost calculation and flow dict construction
###########################################################################

del DEAF.edge_flow[DEAF.edge_count :]
flow_cost = sum(w * x for w, x in zip(DEAF.edge_weights, DEAF.edge_flow))
flow_dict = {n: {} for n in DEAF.node_list}

d = flow_dict[e]
for k in e[1:-2]:
try:
d = d[k]
except KeyError:
t = {}
d[k] = t
d = t
d[e[-2]] = e[-1]

DEAF.edge_sources = (
DEAF.node_list[s] for s in DEAF.edge_sources
)  # Use original nodes.
DEAF.edge_targets = (
DEAF.node_list[t] for t in DEAF.edge_targets
)  # Use original nodes.
if not multigraph:
for e in zip(DEAF.edge_sources, DEAF.edge_targets, DEAF.edge_flow):
edges = G.edges(data=True)
else:
for e in zip(
DEAF.edge_sources, DEAF.edge_targets, DEAF.edge_keys, DEAF.edge_flow
):
edges = G.edges(data=True, keys=True)
for e in edges:
if e != e:
if e[-1].get(capacity, inf) == 0: