Version 5.0 The LEDA User Manual

inline NT MAX FLOW GRH T(const graph& G, node s, node t,
const edge array& cap, edge array& flow,
SET & U, int& num pushes,
int& num edge inspections, int& num relabels,
int& num global relabels, float h)
The preflow push method with the distinction between low and high nodes,
the local relabeling heuristic, the global relabeling heuristic, and the two-phase
approach. The global relabeling heuristic is applied every h·m edge inspections.
template
inline NT MAX FLOW GAP T(const graph& G, node s, node t,
const edge array& cap, edge array& flow,
SET & U, int& num pushes,
int& num edge inspections, int& num relabels,
int& num global relabels, int& num gaps, float h)
The preflow push method with the distinction between low and high nodes,
the local relabeling heuristic, the global relabeling heuristic, the two-phase approach,
and the gap heuristic. The global relabeling heuristic is applied every
h · m edge inspections.
The following generators can be used to generate max-flow problems with integer capacities.
The generators return a GRAPH G and nodes s and t. The edge capacities
are available as edge data, i.e., the capacity of edge e is available as G[e] and the edge array
of capacities is passed as G.edge data( ). For detailed information about the generators
we refer to the LEDA book.
void max flow gen rand(GRAPH & G, node& s, node& t, int n, int m)
A random graph with n nodes, m edges, and random edge capacities in [2,11]
for the edges out of s and in [1,10] for all other edges.
void max flow gen CG1(GRAPH & G, node& s, node& t, int n)
A generator suggested by Cherkassky and Goldberg.
void max flow gen CG2(GRAPH & G, node& s, node& t, int n)
Another generator suggested by Cherkassky and Goldberg.
void max flow gen AMO(GRAPH & G, node& s, node& t, int n)
A generator suggested by Ahuja, Magnanti, and Orlin.