Merge #181, #323
authorAlpar Juttner <alpar@cs.elte.hu>
Wed, 03 Mar 2010 17:14:17 +0000
changeset 9305df6a8f29d5e
parent 923 e77b621e6e7e
parent 927 9a7e4e606f83
child 931 abb95d48e89e
Merge #181, #323
     1.1 --- a/lemon/suurballe.h	Mon Mar 01 07:51:45 2010 +0100
     1.2 +++ b/lemon/suurballe.h	Wed Mar 03 17:14:17 2010 +0000
     1.3 @@ -29,6 +29,7 @@
     1.4  #include <lemon/bin_heap.h>
     1.5  #include <lemon/path.h>
     1.6  #include <lemon/list_graph.h>
     1.7 +#include <lemon/dijkstra.h>
     1.8  #include <lemon/maps.h>
     1.9  
    1.10  namespace lemon {
    1.11 @@ -46,7 +47,7 @@
    1.12    /// Note that this problem is a special case of the \ref min_cost_flow
    1.13    /// "minimum cost flow problem". This implementation is actually an
    1.14    /// efficient specialized version of the \ref CapacityScaling
    1.15 -  /// "Successive Shortest Path" algorithm directly for this problem.
    1.16 +  /// "successive shortest path" algorithm directly for this problem.
    1.17    /// Therefore this class provides query functions for flow values and
    1.18    /// node potentials (the dual solution) just like the minimum cost flow
    1.19    /// algorithms.
    1.20 @@ -55,9 +56,9 @@
    1.21    /// \tparam LEN The type of the length map.
    1.22    /// The default value is <tt>GR::ArcMap<int></tt>.
    1.23    ///
    1.24 -  /// \warning Length values should be \e non-negative \e integers.
    1.25 +  /// \warning Length values should be \e non-negative.
    1.26    ///
    1.27 -  /// \note For finding node-disjoint paths this algorithm can be used
    1.28 +  /// \note For finding \e node-disjoint paths, this algorithm can be used
    1.29    /// along with the \ref SplitNodes adaptor.
    1.30  #ifdef DOXYGEN
    1.31    template <typename GR, typename LEN>
    1.32 @@ -97,6 +98,9 @@
    1.33  
    1.34    private:
    1.35  
    1.36 +    typedef typename Digraph::template NodeMap<int> HeapCrossRef;
    1.37 +    typedef BinHeap<Length, HeapCrossRef> Heap;
    1.38 +
    1.39      // ResidualDijkstra is a special implementation of the
    1.40      // Dijkstra algorithm for finding shortest paths in the
    1.41      // residual network with respect to the reduced arc lengths
    1.42 @@ -104,44 +108,38 @@
    1.43      // distance of the nodes.
    1.44      class ResidualDijkstra
    1.45      {
    1.46 -      typedef typename Digraph::template NodeMap<int> HeapCrossRef;
    1.47 -      typedef BinHeap<Length, HeapCrossRef> Heap;
    1.48 -
    1.49      private:
    1.50  
    1.51 -      // The digraph the algorithm runs on
    1.52        const Digraph &_graph;
    1.53 -
    1.54 -      // The main maps
    1.55 +      const LengthMap &_length;
    1.56        const FlowMap &_flow;
    1.57 -      const LengthMap &_length;
    1.58 -      PotentialMap &_potential;
    1.59 -
    1.60 -      // The distance map
    1.61 -      PotentialMap _dist;
    1.62 -      // The pred arc map
    1.63 +      PotentialMap &_pi;
    1.64        PredMap &_pred;
    1.65 -      // The processed (i.e. permanently labeled) nodes
    1.66 -      std::vector<Node> _proc_nodes;
    1.67 -
    1.68        Node _s;
    1.69        Node _t;
    1.70 +      
    1.71 +      PotentialMap _dist;
    1.72 +      std::vector<Node> _proc_nodes;
    1.73  
    1.74      public:
    1.75  
    1.76 -      /// Constructor.
    1.77 -      ResidualDijkstra( const Digraph &graph,
    1.78 -                        const FlowMap &flow,
    1.79 -                        const LengthMap &length,
    1.80 -                        PotentialMap &potential,
    1.81 -                        PredMap &pred,
    1.82 -                        Node s, Node t ) :
    1.83 -        _graph(graph), _flow(flow), _length(length), _potential(potential),
    1.84 -        _dist(graph), _pred(pred), _s(s), _t(t) {}
    1.85 +      // Constructor
    1.86 +      ResidualDijkstra(Suurballe &srb) :
    1.87 +        _graph(srb._graph), _length(srb._length),
    1.88 +        _flow(*srb._flow), _pi(*srb._potential), _pred(srb._pred), 
    1.89 +        _s(srb._s), _t(srb._t), _dist(_graph) {}
    1.90 +        
    1.91 +      // Run the algorithm and return true if a path is found
    1.92 +      // from the source node to the target node.
    1.93 +      bool run(int cnt) {
    1.94 +        return cnt == 0 ? startFirst() : start();
    1.95 +      }
    1.96  
    1.97 -      /// \brief Run the algorithm. It returns \c true if a path is found
    1.98 -      /// from the source node to the target node.
    1.99 -      bool run() {
   1.100 +    private:
   1.101 +    
   1.102 +      // Execute the algorithm for the first time (the flow and potential
   1.103 +      // functions have to be identically zero).
   1.104 +      bool startFirst() {
   1.105          HeapCrossRef heap_cross_ref(_graph, Heap::PRE_HEAP);
   1.106          Heap heap(heap_cross_ref);
   1.107          heap.push(_s, 0);
   1.108 @@ -151,29 +149,74 @@
   1.109          // Process nodes
   1.110          while (!heap.empty() && heap.top() != _t) {
   1.111            Node u = heap.top(), v;
   1.112 -          Length d = heap.prio() + _potential[u], nd;
   1.113 +          Length d = heap.prio(), dn;
   1.114            _dist[u] = heap.prio();
   1.115 +          _proc_nodes.push_back(u);
   1.116            heap.pop();
   1.117 +
   1.118 +          // Traverse outgoing arcs
   1.119 +          for (OutArcIt e(_graph, u); e != INVALID; ++e) {
   1.120 +            v = _graph.target(e);
   1.121 +            switch(heap.state(v)) {
   1.122 +              case Heap::PRE_HEAP:
   1.123 +                heap.push(v, d + _length[e]);
   1.124 +                _pred[v] = e;
   1.125 +                break;
   1.126 +              case Heap::IN_HEAP:
   1.127 +                dn = d + _length[e];
   1.128 +                if (dn < heap[v]) {
   1.129 +                  heap.decrease(v, dn);
   1.130 +                  _pred[v] = e;
   1.131 +                }
   1.132 +                break;
   1.133 +              case Heap::POST_HEAP:
   1.134 +                break;
   1.135 +            }
   1.136 +          }
   1.137 +        }
   1.138 +        if (heap.empty()) return false;
   1.139 +
   1.140 +        // Update potentials of processed nodes
   1.141 +        Length t_dist = heap.prio();
   1.142 +        for (int i = 0; i < int(_proc_nodes.size()); ++i)
   1.143 +          _pi[_proc_nodes[i]] = _dist[_proc_nodes[i]] - t_dist;
   1.144 +        return true;
   1.145 +      }
   1.146 +
   1.147 +      // Execute the algorithm.
   1.148 +      bool start() {
   1.149 +        HeapCrossRef heap_cross_ref(_graph, Heap::PRE_HEAP);
   1.150 +        Heap heap(heap_cross_ref);
   1.151 +        heap.push(_s, 0);
   1.152 +        _pred[_s] = INVALID;
   1.153 +        _proc_nodes.clear();
   1.154 +
   1.155 +        // Process nodes
   1.156 +        while (!heap.empty() && heap.top() != _t) {
   1.157 +          Node u = heap.top(), v;
   1.158 +          Length d = heap.prio() + _pi[u], dn;
   1.159 +          _dist[u] = heap.prio();
   1.160            _proc_nodes.push_back(u);
   1.161 +          heap.pop();
   1.162  
   1.163            // Traverse outgoing arcs
   1.164            for (OutArcIt e(_graph, u); e != INVALID; ++e) {
   1.165              if (_flow[e] == 0) {
   1.166                v = _graph.target(e);
   1.167                switch(heap.state(v)) {
   1.168 -              case Heap::PRE_HEAP:
   1.169 -                heap.push(v, d + _length[e] - _potential[v]);
   1.170 -                _pred[v] = e;
   1.171 -                break;
   1.172 -              case Heap::IN_HEAP:
   1.173 -                nd = d + _length[e] - _potential[v];
   1.174 -                if (nd < heap[v]) {
   1.175 -                  heap.decrease(v, nd);
   1.176 +                case Heap::PRE_HEAP:
   1.177 +                  heap.push(v, d + _length[e] - _pi[v]);
   1.178                    _pred[v] = e;
   1.179 -                }
   1.180 -                break;
   1.181 -              case Heap::POST_HEAP:
   1.182 -                break;
   1.183 +                  break;
   1.184 +                case Heap::IN_HEAP:
   1.185 +                  dn = d + _length[e] - _pi[v];
   1.186 +                  if (dn < heap[v]) {
   1.187 +                    heap.decrease(v, dn);
   1.188 +                    _pred[v] = e;
   1.189 +                  }
   1.190 +                  break;
   1.191 +                case Heap::POST_HEAP:
   1.192 +                  break;
   1.193                }
   1.194              }
   1.195            }
   1.196 @@ -183,19 +226,19 @@
   1.197              if (_flow[e] == 1) {
   1.198                v = _graph.source(e);
   1.199                switch(heap.state(v)) {
   1.200 -              case Heap::PRE_HEAP:
   1.201 -                heap.push(v, d - _length[e] - _potential[v]);
   1.202 -                _pred[v] = e;
   1.203 -                break;
   1.204 -              case Heap::IN_HEAP:
   1.205 -                nd = d - _length[e] - _potential[v];
   1.206 -                if (nd < heap[v]) {
   1.207 -                  heap.decrease(v, nd);
   1.208 +                case Heap::PRE_HEAP:
   1.209 +                  heap.push(v, d - _length[e] - _pi[v]);
   1.210                    _pred[v] = e;
   1.211 -                }
   1.212 -                break;
   1.213 -              case Heap::POST_HEAP:
   1.214 -                break;
   1.215 +                  break;
   1.216 +                case Heap::IN_HEAP:
   1.217 +                  dn = d - _length[e] - _pi[v];
   1.218 +                  if (dn < heap[v]) {
   1.219 +                    heap.decrease(v, dn);
   1.220 +                    _pred[v] = e;
   1.221 +                  }
   1.222 +                  break;
   1.223 +                case Heap::POST_HEAP:
   1.224 +                  break;
   1.225                }
   1.226              }
   1.227            }
   1.228 @@ -205,7 +248,7 @@
   1.229          // Update potentials of processed nodes
   1.230          Length t_dist = heap.prio();
   1.231          for (int i = 0; i < int(_proc_nodes.size()); ++i)
   1.232 -          _potential[_proc_nodes[i]] += _dist[_proc_nodes[i]] - t_dist;
   1.233 +          _pi[_proc_nodes[i]] += _dist[_proc_nodes[i]] - t_dist;
   1.234          return true;
   1.235        }
   1.236  
   1.237 @@ -226,19 +269,21 @@
   1.238      bool _local_potential;
   1.239  
   1.240      // The source node
   1.241 -    Node _source;
   1.242 +    Node _s;
   1.243      // The target node
   1.244 -    Node _target;
   1.245 +    Node _t;
   1.246  
   1.247      // Container to store the found paths
   1.248 -    std::vector< SimplePath<Digraph> > paths;
   1.249 +    std::vector<Path> _paths;
   1.250      int _path_num;
   1.251  
   1.252      // The pred arc map
   1.253      PredMap _pred;
   1.254 -    // Implementation of the Dijkstra algorithm for finding augmenting
   1.255 -    // shortest paths in the residual network
   1.256 -    ResidualDijkstra *_dijkstra;
   1.257 +    
   1.258 +    // Data for full init
   1.259 +    PotentialMap *_init_dist;
   1.260 +    PredMap *_init_pred;
   1.261 +    bool _full_init;
   1.262  
   1.263    public:
   1.264  
   1.265 @@ -251,17 +296,16 @@
   1.266      Suurballe( const Digraph &graph,
   1.267                 const LengthMap &length ) :
   1.268        _graph(graph), _length(length), _flow(0), _local_flow(false),
   1.269 -      _potential(0), _local_potential(false), _pred(graph)
   1.270 -    {
   1.271 -      LEMON_ASSERT(std::numeric_limits<Length>::is_integer,
   1.272 -        "The length type of Suurballe must be integer");
   1.273 -    }
   1.274 +      _potential(0), _local_potential(false), _pred(graph),
   1.275 +      _init_dist(0), _init_pred(0)
   1.276 +    {}
   1.277  
   1.278      /// Destructor.
   1.279      ~Suurballe() {
   1.280        if (_local_flow) delete _flow;
   1.281        if (_local_potential) delete _potential;
   1.282 -      delete _dijkstra;
   1.283 +      delete _init_dist;
   1.284 +      delete _init_pred;
   1.285      }
   1.286  
   1.287      /// \brief Set the flow map.
   1.288 @@ -306,10 +350,13 @@
   1.289  
   1.290      /// \name Execution Control
   1.291      /// The simplest way to execute the algorithm is to call the run()
   1.292 -    /// function.
   1.293 -    /// \n
   1.294 +    /// function.\n
   1.295 +    /// If you need to execute the algorithm many times using the same
   1.296 +    /// source node, then you may call fullInit() once and start()
   1.297 +    /// for each target node.\n
   1.298      /// If you only need the flow that is the union of the found
   1.299 -    /// arc-disjoint paths, you may call init() and findFlow().
   1.300 +    /// arc-disjoint paths, then you may call findFlow() instead of
   1.301 +    /// start().
   1.302  
   1.303      /// @{
   1.304  
   1.305 @@ -329,23 +376,21 @@
   1.306      /// just a shortcut of the following code.
   1.307      /// \code
   1.308      ///   s.init(s);
   1.309 -    ///   s.findFlow(t, k);
   1.310 -    ///   s.findPaths();
   1.311 +    ///   s.start(t, k);
   1.312      /// \endcode
   1.313      int run(const Node& s, const Node& t, int k = 2) {
   1.314        init(s);
   1.315 -      findFlow(t, k);
   1.316 -      findPaths();
   1.317 +      start(t, k);
   1.318        return _path_num;
   1.319      }
   1.320  
   1.321      /// \brief Initialize the algorithm.
   1.322      ///
   1.323 -    /// This function initializes the algorithm.
   1.324 +    /// This function initializes the algorithm with the given source node.
   1.325      ///
   1.326      /// \param s The source node.
   1.327      void init(const Node& s) {
   1.328 -      _source = s;
   1.329 +      _s = s;
   1.330  
   1.331        // Initialize maps
   1.332        if (!_flow) {
   1.333 @@ -356,8 +401,63 @@
   1.334          _potential = new PotentialMap(_graph);
   1.335          _local_potential = true;
   1.336        }
   1.337 -      for (ArcIt e(_graph); e != INVALID; ++e) (*_flow)[e] = 0;
   1.338 -      for (NodeIt n(_graph); n != INVALID; ++n) (*_potential)[n] = 0;
   1.339 +      _full_init = false;
   1.340 +    }
   1.341 +
   1.342 +    /// \brief Initialize the algorithm and perform Dijkstra.
   1.343 +    ///
   1.344 +    /// This function initializes the algorithm and performs a full
   1.345 +    /// Dijkstra search from the given source node. It makes consecutive
   1.346 +    /// executions of \ref start() "start(t, k)" faster, since they
   1.347 +    /// have to perform %Dijkstra only k-1 times.
   1.348 +    ///
   1.349 +    /// This initialization is usually worth using instead of \ref init()
   1.350 +    /// if the algorithm is executed many times using the same source node.
   1.351 +    ///
   1.352 +    /// \param s The source node.
   1.353 +    void fullInit(const Node& s) {
   1.354 +      // Initialize maps
   1.355 +      init(s);
   1.356 +      if (!_init_dist) {
   1.357 +        _init_dist = new PotentialMap(_graph);
   1.358 +      }
   1.359 +      if (!_init_pred) {
   1.360 +        _init_pred = new PredMap(_graph);
   1.361 +      }
   1.362 +
   1.363 +      // Run a full Dijkstra
   1.364 +      typename Dijkstra<Digraph, LengthMap>
   1.365 +        ::template SetStandardHeap<Heap>
   1.366 +        ::template SetDistMap<PotentialMap>
   1.367 +        ::template SetPredMap<PredMap>
   1.368 +        ::Create dijk(_graph, _length);
   1.369 +      dijk.distMap(*_init_dist).predMap(*_init_pred);
   1.370 +      dijk.run(s);
   1.371 +      
   1.372 +      _full_init = true;
   1.373 +    }
   1.374 +
   1.375 +    /// \brief Execute the algorithm.
   1.376 +    ///
   1.377 +    /// This function executes the algorithm.
   1.378 +    ///
   1.379 +    /// \param t The target node.
   1.380 +    /// \param k The number of paths to be found.
   1.381 +    ///
   1.382 +    /// \return \c k if there are at least \c k arc-disjoint paths from
   1.383 +    /// \c s to \c t in the digraph. Otherwise it returns the number of
   1.384 +    /// arc-disjoint paths found.
   1.385 +    ///
   1.386 +    /// \note Apart from the return value, <tt>s.start(t, k)</tt> is
   1.387 +    /// just a shortcut of the following code.
   1.388 +    /// \code
   1.389 +    ///   s.findFlow(t, k);
   1.390 +    ///   s.findPaths();
   1.391 +    /// \endcode
   1.392 +    int start(const Node& t, int k = 2) {
   1.393 +      findFlow(t, k);
   1.394 +      findPaths();
   1.395 +      return _path_num;
   1.396      }
   1.397  
   1.398      /// \brief Execute the algorithm to find an optimal flow.
   1.399 @@ -375,20 +475,39 @@
   1.400      ///
   1.401      /// \pre \ref init() must be called before using this function.
   1.402      int findFlow(const Node& t, int k = 2) {
   1.403 -      _target = t;
   1.404 -      _dijkstra =
   1.405 -        new ResidualDijkstra( _graph, *_flow, _length, *_potential, _pred,
   1.406 -                              _source, _target );
   1.407 +      _t = t;
   1.408 +      ResidualDijkstra dijkstra(*this);
   1.409 +      
   1.410 +      // Initialization
   1.411 +      for (ArcIt e(_graph); e != INVALID; ++e) {
   1.412 +        (*_flow)[e] = 0;
   1.413 +      }
   1.414 +      if (_full_init) {
   1.415 +        for (NodeIt n(_graph); n != INVALID; ++n) {
   1.416 +          (*_potential)[n] = (*_init_dist)[n];
   1.417 +        }
   1.418 +        Node u = _t;
   1.419 +        Arc e;
   1.420 +        while ((e = (*_init_pred)[u]) != INVALID) {
   1.421 +          (*_flow)[e] = 1;
   1.422 +          u = _graph.source(e);
   1.423 +        }        
   1.424 +        _path_num = 1;
   1.425 +      } else {
   1.426 +        for (NodeIt n(_graph); n != INVALID; ++n) {
   1.427 +          (*_potential)[n] = 0;
   1.428 +        }
   1.429 +        _path_num = 0;
   1.430 +      }
   1.431  
   1.432        // Find shortest paths
   1.433 -      _path_num = 0;
   1.434        while (_path_num < k) {
   1.435          // Run Dijkstra
   1.436 -        if (!_dijkstra->run()) break;
   1.437 +        if (!dijkstra.run(_path_num)) break;
   1.438          ++_path_num;
   1.439  
   1.440          // Set the flow along the found shortest path
   1.441 -        Node u = _target;
   1.442 +        Node u = _t;
   1.443          Arc e;
   1.444          while ((e = _pred[u]) != INVALID) {
   1.445            if (u == _graph.target(e)) {
   1.446 @@ -405,8 +524,8 @@
   1.447  
   1.448      /// \brief Compute the paths from the flow.
   1.449      ///
   1.450 -    /// This function computes the paths from the found minimum cost flow,
   1.451 -    /// which is the union of some arc-disjoint paths.
   1.452 +    /// This function computes arc-disjoint paths from the found minimum
   1.453 +    /// cost flow, which is the union of them.
   1.454      ///
   1.455      /// \pre \ref init() and \ref findFlow() must be called before using
   1.456      /// this function.
   1.457 @@ -414,15 +533,15 @@
   1.458        FlowMap res_flow(_graph);
   1.459        for(ArcIt a(_graph); a != INVALID; ++a) res_flow[a] = (*_flow)[a];
   1.460  
   1.461 -      paths.clear();
   1.462 -      paths.resize(_path_num);
   1.463 +      _paths.clear();
   1.464 +      _paths.resize(_path_num);
   1.465        for (int i = 0; i < _path_num; ++i) {
   1.466 -        Node n = _source;
   1.467 -        while (n != _target) {
   1.468 +        Node n = _s;
   1.469 +        while (n != _t) {
   1.470            OutArcIt e(_graph, n);
   1.471            for ( ; res_flow[e] == 0; ++e) ;
   1.472            n = _graph.target(e);
   1.473 -          paths[i].addBack(e);
   1.474 +          _paths[i].addBack(e);
   1.475            res_flow[e] = 0;
   1.476          }
   1.477        }
   1.478 @@ -520,8 +639,8 @@
   1.479      ///
   1.480      /// \pre \ref run() or \ref findPaths() must be called before using
   1.481      /// this function.
   1.482 -    Path path(int i) const {
   1.483 -      return paths[i];
   1.484 +    const Path& path(int i) const {
   1.485 +      return _paths[i];
   1.486      }
   1.487  
   1.488      /// @}
     2.1 --- a/test/suurballe_test.cc	Mon Mar 01 07:51:45 2010 +0100
     2.2 +++ b/test/suurballe_test.cc	Wed Mar 03 17:14:17 2010 +0000
     2.3 @@ -101,6 +101,9 @@
     2.4    k = suurb_test.run(n, n);
     2.5    k = suurb_test.run(n, n, k);
     2.6    suurb_test.init(n);
     2.7 +  suurb_test.fullInit(n);
     2.8 +  suurb_test.start(n);
     2.9 +  suurb_test.start(n, k);
    2.10    k = suurb_test.findFlow(n);
    2.11    k = suurb_test.findFlow(n, k);
    2.12    suurb_test.findPaths();