我正在尝试使用Ford-Fulkerson算法解决图的最大流问题。该算法仅针对有向图进行描述。如果图是无向的呢？
为了模拟一个无向图，我所做的是在一对顶点之间使用两个有向边。令我困惑的是：这些边中的每一条都应该有一个剩余边吗？还是说“相反”的有向边就是剩余边？
我假设后者，但我的算法似乎进入了一个无限循环。我希望你们中的任何一个能给我一些帮助。下面是我的实现。我在查找中使用DFS。
import sys
import fileinput
class Vertex(object):
def __init__(self, name):
self.name = name
self.edges = []
def find(self, sink, path):
if(self == sink):
return path
for edge in self.edges:
residual = edge.capacity - edge.flow
if(residual > 0 or edge.inf):
if(edge not in path and edge.oppositeEdge not in path):
toVertex = edge.toVertex
path.append(edge)
result = toVertex.find(sink, path)
if result != None:
return result
class Edge(object):
def __init__(self, fromVertex, toVertex, capacity):
self.fromVertex = fromVertex
self.toVertex = toVertex
self.capacity = capacity
self.flow = 0
self.inf = False
if(capacity == -1):
self.inf = True
def __repr__(self):
return self.fromVertex.name.strip() + " - " + self.toVertex.name.strip()
def buildGraph(vertices, edges):
for edge in edges:
sourceVertex = vertices[int(edge[0])]
sinkVertex = vertices[int(edge[1])]
capacity = int(edge[2])
edge1 = Edge(sourceVertex, sinkVertex, capacity)
edge2 = Edge(sinkVertex, sourceVertex, capacity)
sourceVertex.edges.append(edge1)
sinkVertex.edges.append(edge2)
edge1.oppositeEdge = edge2
edge2.oppositeEdge = edge1
def maxFlow(source, sink):
path = source.find(sink, [])
while path != None:
minCap = sys.maxint
for e in path:
if(e.capacity < minCap and not e.inf):
minCap = e.capacity
for edge in path:
edge.flow += minCap
edge.oppositeEdge.flow -= minCap
path = source.find(sink, [])
return sum(e.flow for e in source.edges)
vertices, edges = parse()
buildGraph(vertices, edges)
source = vertices[0]
sink = vertices[len(vertices)-1]
maxFlow = maxFlow(source, sink)
a->b的剩余容量。 - phimuemue