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/* Author: Oren Salzman */
/* Implementation based on
G. Ramalingam and T. W. Reps, On the computational complexity of
dynamic graph problems, Theor. Comput. Sci., vol. 158, no. 1&2, pp.
233-277, 1996.
*/
#ifndef OMPL_DATASTRUCTURES_DYNAMICSSSP_H
#define OMPL_DATASTRUCTURES_DYNAMICSSSP_H
#include <list>
#include <set>
#include <vector>
#include <limits>
#include <boost/graph/graph_traits.hpp>
#include <boost/graph/adjacency_list.hpp>
#include <unordered_set>
namespace ompl
{
class DynamicSSSP
{
public:
DynamicSSSP()
{
graph_ = new Graph(0);
}
~DynamicSSSP(void)
{
delete graph_;
}
void addVertex(std::size_t id)
{
distance_.push_back((id == 0) ? 0 : std::numeric_limits<double>::infinity());
parent_.push_back(NO_ID);
boost::add_vertex(id, *graph_);
return;
}
// we assume that no two paths have the same cost,
// this asssumption is valid when the nodes have some randomeness to them
void addEdge(std::size_t v, std::size_t w, double weight,
bool collectVertices, std::list<std::size_t>& affectedVertices)
{
// first, add edge to graph
WeightProperty edge_property(weight);
boost::add_edge(v, w, edge_property, *graph_);
// now, update distance_
assert ( (distance_[v] == std::numeric_limits<double>::infinity()) ||
(distance_[w] == std::numeric_limits<double>::infinity()) ||
(distance_[w] + weight != distance_[w]) );
std::vector<double> cost( boost::num_vertices(*graph_),
std::numeric_limits<double>::infinity()); // initialize to n values of cost oo
IsLessThan isLessThan(cost);
Queue queue(isLessThan);
if (distance_[v] + weight < distance_[w])
{
distance_[w] = distance_[v] + weight;
parent_[w] = v;
cost[w] = 0;
queue.insert(w);
}
WeightMap weights = boost::get(boost::edge_weight_t(), *graph_);
while (!queue.empty())
{
// pop head of queue
std::size_t u = *(queue.begin());
queue.erase(queue.begin());
if (collectVertices)
affectedVertices.push_back(u);
boost::out_edges(u, *graph_);
// for every outgoing edge, see if we can improve its cost
boost::graph_traits<Graph>::out_edge_iterator ei, ei_end;
for (boost::tie(ei, ei_end) = boost::out_edges(u, *graph_); ei != ei_end; ++ei)
{
std::size_t x = boost::target(*ei, *graph_);
double edgeWeight = boost::get(weights, *ei);
if (distance_[u] + edgeWeight < distance_[x])
{
distance_[x] = distance_[u] + edgeWeight;
parent_[x] = u;
// insert to queue
QueueIter qIter = queue.find(x);
if (qIter != queue.end() )
queue.erase(qIter);
cost[x] = distance_[x] - distance_[v];
queue.insert(x);
}
}
}
return;
}
void removeEdge(std::size_t v, std::size_t w,
bool collectVertices, std::list<std::size_t>& affectedVertices)
{
// first, remove edge from graph
boost::remove_edge(v, w, *graph_);
if (parent_[w] != v)
return;
// Phase 1: Identify the affected vertices and remove the affected edges from SP(G)
std::list<std::size_t> workSet;
IntSet affectedVerticesSet;
workSet.push_back(w);
while (!workSet.empty())
{
//S elect and remove a vertex u from WorkSet
std::size_t u = workSet.front();
workSet.pop_front();
affectedVerticesSet.insert(u);
boost::graph_traits<Graph>::out_edge_iterator ei, ei_end;
for (boost::tie(ei, ei_end) = boost::out_edges(u, *graph_); ei != ei_end; ++ei)
{
std::size_t x = boost::target(*ei, *graph_);
if (parent_[x] == u)
workSet.push_back(x);
}
}
WeightMap weights = boost::get(boost::edge_weight_t(), *graph_);
// Phase 2: Determine new distances from affected vertices to source(G) and update SP(G).
IsLessThan isLessThan(distance_);
Queue queue(isLessThan);
for (IntSetIter set_iter = affectedVerticesSet.begin(); set_iter!= affectedVerticesSet.end(); ++set_iter)
{
std::size_t a = *set_iter;
distance_[a] = std::numeric_limits<double>::infinity();
// go over all incoming neighbors which are NOT affected vertices
// get the best such neighbor
boost::graph_traits<Graph>::in_edge_iterator ei, ei_end;
for (boost::tie(ei, ei_end) = boost::in_edges(a, *graph_); ei != ei_end; ++ei)
{
std::size_t b = boost::source(*ei, *graph_);
if (affectedVerticesSet.find(b) == affectedVerticesSet.end())
{
double edgeWeight = boost::get(weights, *ei);
if (distance_[b] + edgeWeight < distance_[a])
{
distance_[a] = distance_[b] + edgeWeight;
parent_[a] = b;
}
}
}
if (distance_[a] != std::numeric_limits<double>::infinity())
queue.insert(a);
}
while(!queue.empty())
{
// pop head of queue
std::size_t a = *queue.begin();
queue.erase(queue.begin());
if (collectVertices)
affectedVertices.push_back(a);
// for every outgoing edge, see if we can improve its cost
boost::graph_traits<Graph>::out_edge_iterator ei, ei_end;
for (boost::tie(ei, ei_end) = boost::out_edges(a, *graph_); ei != ei_end; ++ei)
{
int c = boost::target(*ei, *graph_);
double edgeWeight = boost::get(weights, *ei);
if (distance_[a] + edgeWeight < distance_[c])
{
distance_[c] = distance_[a] + edgeWeight;
parent_[c] = a;
// insert to queue
QueueIter qIter = queue.find(c);
if (qIter != queue.end() )
queue.erase(qIter);
queue.insert(c);
}
}
}
return;
}
double getShortestPathCost(std::size_t u) const
{
return this->distance_[u];
}
std::size_t getShortestPathParent(std::size_t u) const
{
return parent_[u];
}
private:
typedef boost::property<boost::edge_weight_t, double> WeightProperty;
typedef boost::adjacency_list<boost::vecS, // container type for the edge list
boost::vecS, // container type for the vertex list
boost::bidirectionalS, // directedS / undirectedS / bidirectionalS
std::size_t, // vertex properties
WeightProperty // edge properties
> Graph;
typedef boost::property_map<Graph, boost::edge_weight_t>::type WeightMap;
static const int NO_ID = -1;
class IsLessThan
{
public:
IsLessThan(std::vector<double>& cost)
:cost_(cost)
{
}
bool operator()(std::size_t id1, std::size_t id2) const
{
return (cost_[id1] < cost_[id2]);
}
private:
std::vector<double>& cost_;
}; //IsLessThan
typedef std::set<std::size_t, IsLessThan> Queue;
typedef Queue::iterator QueueIter;
typedef std::unordered_set<std::size_t> IntSet;
typedef IntSet::iterator IntSetIter;
Graph* graph_;
/// \brief distance from source which is node zero
std::vector<double> distance_;
/// \brief parent of each node
std::vector<std::size_t> parent_;
}; //DynamicSSSP
}
#endif //OMPL_DATASTRUCTURES_DYNAMICSSSP_H
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