📄 push_relabel_max_flow.hpp
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distance_size_type r; // distance of layer before the current layer
r = empty_distance - 1;
// Set the distance for the vertices beyond the gap to "infinity".
for (layer_iterator l = layers.begin() + empty_distance + 1;
l < layers.begin() + max_distance; ++l) {
list_iterator i;
for (i = l->inactive_vertices.begin();
i != l->inactive_vertices.end(); ++i) {
distance[*i] = n;
++gap_node_count;
}
l->inactive_vertices.clear();
}
max_distance = r;
max_active = r;
}
//=======================================================================
// This is the core part of the algorithm, "phase one".
FlowValue maximum_preflow()
{
work_since_last_update = 0;
while (max_active >= min_active) { // "main" loop
Layer& layer = layers[max_active];
list_iterator u_iter = layer.active_vertices.begin();
if (u_iter == layer.active_vertices.end())
--max_active;
else {
vertex_descriptor u = *u_iter;
remove_from_active_list(u);
discharge(u);
if (work_since_last_update * global_update_frequency() > nm) {
global_distance_update();
work_since_last_update = 0;
}
}
} // while (max_active >= min_active)
return excess_flow[sink];
} // maximum_preflow()
//=======================================================================
// remove excess flow, the "second phase"
// This does a DFS on the reverse flow graph of nodes with excess flow.
// If a cycle is found, cancel it.
// Return the nodes with excess flow in topological order.
//
// Unlike the prefl_to_flow() implementation, we use
// "color" instead of "distance" for the DFS labels
// "parent" instead of nl_prev for the DFS tree
// "topo_next" instead of nl_next for the topological ordering
void convert_preflow_to_flow()
{
vertex_iterator u_iter, u_end;
out_edge_iterator ai, a_end;
vertex_descriptor r, restart, u;
std::vector<vertex_descriptor> parent(n);
std::vector<vertex_descriptor> topo_next(n);
vertex_descriptor tos(parent[0]),
bos(parent[0]); // bogus initialization, just to avoid warning
bool bos_null = true;
// handle self-loops
for (tie(u_iter, u_end) = vertices(g); u_iter != u_end; ++u_iter)
for (tie(ai, a_end) = out_edges(*u_iter, g); ai != a_end; ++ai)
if (target(*ai, g) == *u_iter)
residual_capacity[*ai] = capacity[*ai];
// initialize
for (tie(u_iter, u_end) = vertices(g); u_iter != u_end; ++u_iter) {
u = *u_iter;
color[u] = ColorTraits::white();
parent[u] = u;
current[u] = out_edges(u, g).first;
}
// eliminate flow cycles and topologically order the vertices
for (tie(u_iter, u_end) = vertices(g); u_iter != u_end; ++u_iter) {
u = *u_iter;
if (color[u] == ColorTraits::white()
&& excess_flow[u] > 0
&& u != src && u != sink ) {
r = u;
color[r] = ColorTraits::gray();
while (1) {
for (; current[u] != out_edges(u, g).second; ++current[u]) {
edge_descriptor a = *current[u];
if (capacity[a] == 0 && is_residual_edge(a)) {
vertex_descriptor v = target(a, g);
if (color[v] == ColorTraits::white()) {
color[v] = ColorTraits::gray();
parent[v] = u;
u = v;
break;
} else if (color[v] == ColorTraits::gray()) {
// find minimum flow on the cycle
FlowValue delta = residual_capacity[a];
while (1) {
BOOST_USING_STD_MIN();
delta = min BOOST_PREVENT_MACRO_SUBSTITUTION(delta, residual_capacity[*current[v]]);
if (v == u)
break;
else
v = target(*current[v], g);
}
// remove delta flow units
v = u;
while (1) {
a = *current[v];
residual_capacity[a] -= delta;
residual_capacity[reverse_edge[a]] += delta;
v = target(a, g);
if (v == u)
break;
}
// back-out of DFS to the first saturated edge
restart = u;
for (v = target(*current[u], g); v != u; v = target(a, g)){
a = *current[v];
if (color[v] == ColorTraits::white()
|| is_saturated(a)) {
color[target(*current[v], g)] = ColorTraits::white();
if (color[v] != ColorTraits::white())
restart = v;
}
}
if (restart != u) {
u = restart;
++current[u];
break;
}
} // else if (color[v] == ColorTraits::gray())
} // if (capacity[a] == 0 ...
} // for out_edges(u, g) (though "u" changes during loop)
if (current[u] == out_edges(u, g).second) {
// scan of i is complete
color[u] = ColorTraits::black();
if (u != src) {
if (bos_null) {
bos = u;
bos_null = false;
tos = u;
} else {
topo_next[u] = tos;
tos = u;
}
}
if (u != r) {
u = parent[u];
++current[u];
} else
break;
}
} // while (1)
} // if (color[u] == white && excess_flow[u] > 0 & ...)
} // for all vertices in g
// return excess flows
// note that the sink is not on the stack
if (! bos_null) {
for (u = tos; u != bos; u = topo_next[u]) {
ai = out_edges(u, g).first;
while (excess_flow[u] > 0 && ai != out_edges(u, g).second) {
if (capacity[*ai] == 0 && is_residual_edge(*ai))
push_flow(*ai);
++ai;
}
}
// do the bottom
u = bos;
ai = out_edges(u, g).first;
while (excess_flow[u] > 0) {
if (capacity[*ai] == 0 && is_residual_edge(*ai))
push_flow(*ai);
++ai;
}
}
} // convert_preflow_to_flow()
//=======================================================================
inline bool is_flow()
{
vertex_iterator u_iter, u_end;
out_edge_iterator ai, a_end;
// check edge flow values
for (tie(u_iter, u_end) = vertices(g); u_iter != u_end; ++u_iter) {
for (tie(ai, a_end) = out_edges(*u_iter, g); ai != a_end; ++ai) {
edge_descriptor a = *ai;
if (capacity[a] > 0)
if ((residual_capacity[a] + residual_capacity[reverse_edge[a]]
!= capacity[a] + capacity[reverse_edge[a]])
|| (residual_capacity[a] < 0)
|| (residual_capacity[reverse_edge[a]] < 0))
return false;
}
}
// check conservation
FlowValue sum;
for (tie(u_iter, u_end) = vertices(g); u_iter != u_end; ++u_iter) {
vertex_descriptor u = *u_iter;
if (u != src && u != sink) {
if (excess_flow[u] != 0)
return false;
sum = 0;
for (tie(ai, a_end) = out_edges(u, g); ai != a_end; ++ai)
if (capacity[*ai] > 0)
sum -= capacity[*ai] - residual_capacity[*ai];
else
sum += residual_capacity[*ai];
if (excess_flow[u] != sum)
return false;
}
}
return true;
} // is_flow()
bool is_optimal() {
// check if mincut is saturated...
global_distance_update();
return distance[src] >= n;
}
void print_statistics(std::ostream& os) const {
os << "pushes: " << push_count << std::endl
<< "relabels: " << relabel_count << std::endl
<< "updates: " << update_count << std::endl
<< "gaps: " << gap_count << std::endl
<< "gap nodes: " << gap_node_count << std::endl
<< std::endl;
}
void print_flow_values(std::ostream& os) const {
os << "flow values" << std::endl;
vertex_iterator u_iter, u_end;
out_edge_iterator ei, e_end;
for (tie(u_iter, u_end) = vertices(g); u_iter != u_end; ++u_iter)
for (tie(ei, e_end) = out_edges(*u_iter, g); ei != e_end; ++ei)
if (capacity[*ei] > 0)
os << *u_iter << " " << target(*ei, g) << " "
<< (capacity[*ei] - residual_capacity[*ei]) << std::endl;
os << std::endl;
}
//=======================================================================
Graph& g;
vertices_size_type n;
vertices_size_type nm;
EdgeCapacityMap capacity;
vertex_descriptor src;
vertex_descriptor sink;
VertexIndexMap index;
// will need to use random_access_property_map with these
std::vector< FlowValue > excess_flow;
std::vector< out_edge_iterator > current;
std::vector< distance_size_type > distance;
std::vector< default_color_type > color;
// Edge Property Maps that must be interior to the graph
ReverseEdgeMap reverse_edge;
ResidualCapacityEdgeMap residual_capacity;
LayerArray layers;
std::vector< list_iterator > layer_list_ptr;
distance_size_type max_distance; // maximal distance
distance_size_type max_active; // maximal distance with active node
distance_size_type min_active; // minimal distance with active node
boost::queue<vertex_descriptor> Q;
// Statistics counters
long push_count;
long update_count;
long relabel_count;
long gap_count;
long gap_node_count;
inline double global_update_frequency() { return 0.5; }
inline vertices_size_type alpha() { return 6; }
inline long beta() { return 12; }
long work_since_last_update;
};
} // namespace detail
template <class Graph,
class CapacityEdgeMap, class ResidualCapacityEdgeMap,
class ReverseEdgeMap, class VertexIndexMap>
typename property_traits<CapacityEdgeMap>::value_type
push_relabel_max_flow
(Graph& g,
typename graph_traits<Graph>::vertex_descriptor src,
typename graph_traits<Graph>::vertex_descriptor sink,
CapacityEdgeMap cap, ResidualCapacityEdgeMap res,
ReverseEdgeMap rev, VertexIndexMap index_map)
{
typedef typename property_traits<CapacityEdgeMap>::value_type FlowValue;
detail::push_relabel<Graph, CapacityEdgeMap, ResidualCapacityEdgeMap,
ReverseEdgeMap, VertexIndexMap, FlowValue>
algo(g, cap, res, rev, src, sink, index_map);
FlowValue flow = algo.maximum_preflow();
algo.convert_preflow_to_flow();
assert(algo.is_flow());
assert(algo.is_optimal());
return flow;
} // push_relabel_max_flow()
template <class Graph, class P, class T, class R>
typename detail::edge_capacity_value<Graph, P, T, R>::type
push_relabel_max_flow
(Graph& g,
typename graph_traits<Graph>::vertex_descriptor src,
typename graph_traits<Graph>::vertex_descriptor sink,
const bgl_named_params<P, T, R>& params)
{
return push_relabel_max_flow
(g, src, sink,
choose_const_pmap(get_param(params, edge_capacity), g, edge_capacity),
choose_pmap(get_param(params, edge_residual_capacity),
g, edge_residual_capacity),
choose_const_pmap(get_param(params, edge_reverse), g, edge_reverse),
choose_const_pmap(get_param(params, vertex_index), g, vertex_index)
);
}
template <class Graph>
typename property_traits<
typename property_map<Graph, edge_capacity_t>::const_type
>::value_type
push_relabel_max_flow
(Graph& g,
typename graph_traits<Graph>::vertex_descriptor src,
typename graph_traits<Graph>::vertex_descriptor sink)
{
bgl_named_params<int, buffer_param_t> params(0); // bogus empty param
return push_relabel_max_flow(g, src, sink, params);
}
} // namespace boost
#endif // BOOST_PUSH_RELABEL_MAX_FLOW_HPP
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