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kpeter (Peter Kovacs)
kpeter@inf.elte.hu
Various doc improvements (#406)
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8 files changed with 32 insertions and 28 deletions:
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\code
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all_lower_case_with_underscores
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\endcode
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\subsection pri-loc-var Private member variables
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Private member variables should start with underscore
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Private member variables should start with underscore.
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\code
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_start_with_underscores
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_start_with_underscore
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\endcode
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\subsection cs-excep Exceptions
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When writing exceptions please comply the following naming conventions.
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   \ref bunnagel98efficient.
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 - \ref CapacityScaling Capacity Scaling algorithm based on the successive
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   shortest path method \ref edmondskarp72theoretical.
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 - \ref CycleCanceling Cycle-Canceling algorithms, two of which are
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   strongly polynomial \ref klein67primal, \ref goldberg89cyclecanceling.
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In general NetworkSimplex is the most efficient implementation,
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but in special cases other algorithms could be faster.
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In general, \ref NetworkSimplex and \ref CostScaling are the most efficient
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implementations, but the other two algorithms could be faster in special cases.
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For example, if the total supply and/or capacities are rather small,
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CapacityScaling is usually the fastest algorithm (without effective scaling).
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\ref CapacityScaling is usually the fastest algorithm (without effective scaling).
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*/
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/**
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@defgroup min_cut Minimum Cut Algorithms
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@ingroup algs
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@@ -468,13 +468,13 @@
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  \ref dasdan98minmeancycle.
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- \ref HartmannOrlinMmc Hartmann-Orlin's algorithm, which is an improved
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  version of Karp's algorithm \ref dasdan98minmeancycle.
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- \ref HowardMmc Howard's policy iteration algorithm
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  \ref dasdan98minmeancycle.
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In practice, the \ref HowardMmc "Howard" algorithm proved to be by far the
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In practice, the \ref HowardMmc "Howard" algorithm turned out to be by far the
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most efficient one, though the best known theoretical bound on its running
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time is exponential.
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Both \ref KarpMmc "Karp" and \ref HartmannOrlinMmc "Hartmann-Orlin" algorithms
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run in time O(ne) and use space O(n<sup>2</sup>+e), but the latter one is
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typically faster due to the applied early termination scheme.
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*/
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@@ -536,13 +536,13 @@
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\image html connected_components.png
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\image latex connected_components.eps "Connected components" width=\textwidth
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*/
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/**
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@defgroup planar Planarity Embedding and Drawing
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@defgroup planar Planar Embedding and Drawing
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@ingroup algs
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\brief Algorithms for planarity checking, embedding and drawing
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This group contains the algorithms for planarity checking,
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embedding and drawing.
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  /// "CapacityScalingDefaultTraits<GR, V, C>".
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  /// In most cases, this parameter should not be set directly,
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  /// consider to use the named template parameters instead.
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  ///
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  /// \warning Both number types must be signed and all input data must
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  /// be integer.
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  /// \warning This algorithm does not support negative costs for such
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  /// arcs that have infinite upper bound.
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  /// \warning This algorithm does not support negative costs for
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  /// arcs having infinite upper bound.
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#ifdef DOXYGEN
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  template <typename GR, typename V, typename C, typename TR>
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#else
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  template < typename GR, typename V = int, typename C = V,
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             typename TR = CapacityScalingDefaultTraits<GR, V, C> >
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#endif
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@@ -419,13 +419,13 @@
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    /// This function sets a single source node and a single target node
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    /// and the required flow value.
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    /// If neither this function nor \ref supplyMap() is used before
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    /// calling \ref run(), the supply of each node will be set to zero.
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    ///
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    /// Using this function has the same effect as using \ref supplyMap()
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    /// with such a map in which \c k is assigned to \c s, \c -k is
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    /// with a map in which \c k is assigned to \c s, \c -k is
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    /// assigned to \c t and all other nodes have zero supply value.
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    ///
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    /// \param s The source node.
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    /// \param t The target node.
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    /// \param k The required amount of flow from node \c s to node \c t
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    /// (i.e. the supply of \c s and the demand of \c t).
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        to.build(from, nodeRefMap, edgeRefMap);
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      }
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    };
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  }
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  /// Check whether a graph is undirected.
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  /// \brief Check whether a graph is undirected.
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  ///
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  /// This function returns \c true if the given graph is undirected.
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#ifdef DOXYGEN
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  template <typename GR>
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  bool undirected(const GR& g) { return false; }
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#else
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  /// "minimum cost flow" \ref amo93networkflows, \ref goldberg90approximation,
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  /// \ref goldberg97efficient, \ref bunnagel98efficient.
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  /// It is a highly efficient primal-dual solution method, which
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  /// can be viewed as the generalization of the \ref Preflow
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  /// "preflow push-relabel" algorithm for the maximum flow problem.
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  ///
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  /// In general, \ref NetworkSimplex and \ref CostScaling are the fastest
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  /// implementations available in LEMON for this problem.
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  ///
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  /// Most of the parameters of the problem (except for the digraph)
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  /// can be given using separate functions, and the algorithm can be
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  /// executed using the \ref run() function. If some parameters are not
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  /// specified, then default values will be used.
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  ///
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  /// \tparam GR The digraph type the algorithm runs on.
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  /// "CostScalingDefaultTraits<GR, V, C>".
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  /// In most cases, this parameter should not be set directly,
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  /// consider to use the named template parameters instead.
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  ///
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  /// \warning Both number types must be signed and all input data must
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  /// be integer.
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  /// \warning This algorithm does not support negative costs for such
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  /// arcs that have infinite upper bound.
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  /// \warning This algorithm does not support negative costs for
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  /// arcs having infinite upper bound.
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  ///
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  /// \note %CostScaling provides three different internal methods,
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  /// from which the most efficient one is used by default.
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  /// For more information, see \ref Method.
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#ifdef DOXYGEN
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  template <typename GR, typename V, typename C, typename TR>
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    /// for the \ref run() function.
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    ///
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    /// \ref CostScaling provides three internal methods that differ mainly
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    /// in their base operations, which are used in conjunction with the
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    /// relabel operation.
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    /// By default, the so called \ref PARTIAL_AUGMENT
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    /// "Partial Augment-Relabel" method is used, which proved to be
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    /// "Partial Augment-Relabel" method is used, which turned out to be
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    /// the most efficient and the most robust on various test inputs.
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    /// However, the other methods can be selected using the \ref run()
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    /// function with the proper parameter.
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    enum Method {
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      /// Local push operations are used, i.e. flow is moved only on one
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      /// admissible arc at once.
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    /// This function sets a single source node and a single target node
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    /// and the required flow value.
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    /// If neither this function nor \ref supplyMap() is used before
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    /// calling \ref run(), the supply of each node will be set to zero.
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    ///
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    /// Using this function has the same effect as using \ref supplyMap()
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    /// with such a map in which \c k is assigned to \c s, \c -k is
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    /// with a map in which \c k is assigned to \c s, \c -k is
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    /// assigned to \c t and all other nodes have zero supply value.
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    ///
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    /// \param s The source node.
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    /// \param t The target node.
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    /// \param k The required amount of flow from node \c s to node \c t
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    /// (i.e. the supply of \c s and the demand of \c t).
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  /// and supply values in the algorithm. By default, it is \c int.
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  /// \tparam C The number type used for costs and potentials in the
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  /// algorithm. By default, it is the same as \c V.
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  ///
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  /// \warning Both number types must be signed and all input data must
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  /// be integer.
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  /// \warning This algorithm does not support negative costs for such
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  /// arcs that have infinite upper bound.
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  /// \warning This algorithm does not support negative costs for
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  /// arcs having infinite upper bound.
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  ///
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  /// \note For more information about the three available methods,
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  /// see \ref Method.
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#ifdef DOXYGEN
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  template <typename GR, typename V, typename C>
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#else
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    ///
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    /// Enum type containing constants for selecting the used method
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    /// for the \ref run() function.
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    ///
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    /// \ref CycleCanceling provides three different cycle-canceling
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    /// methods. By default, \ref CANCEL_AND_TIGHTEN "Cancel and Tighten"
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    /// is used, which proved to be the most efficient and the most robust
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    /// on various test inputs.
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    /// is used, which is by far the most efficient and the most robust.
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    /// However, the other methods can be selected using the \ref run()
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    /// function with the proper parameter.
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    enum Method {
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      /// A simple cycle-canceling method, which uses the
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      /// \ref BellmanFord "Bellman-Ford" algorithm with limited iteration
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      /// number for detecting negative cycles in the residual network.
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    /// This function sets a single source node and a single target node
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    /// and the required flow value.
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    /// If neither this function nor \ref supplyMap() is used before
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    /// calling \ref run(), the supply of each node will be set to zero.
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    ///
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    /// Using this function has the same effect as using \ref supplyMap()
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    /// with such a map in which \c k is assigned to \c s, \c -k is
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    /// with a map in which \c k is assigned to \c s, \c -k is
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    /// assigned to \c t and all other nodes have zero supply value.
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    ///
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    /// \param s The source node.
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    /// \param t The target node.
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    /// \param k The required amount of flow from node \c s to node \c t
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    /// (i.e. the supply of \c s and the demand of \c t).
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///if a (di)graph is \e Eulerian.
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namespace lemon {
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  ///Euler tour iterator for digraphs.
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  /// \ingroup graph_prop
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  /// \ingroup graph_properties
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  ///This iterator provides an Euler tour (Eulerian circuit) of a \e directed
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  ///graph (if there exists) and it converts to the \c Arc type of the digraph.
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  ///
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  ///For example, if the given digraph has an Euler tour (i.e it has only one
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  ///non-trivial component and the in-degree is equal to the out-degree
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  ///for all nodes), then the following code will put the arcs of \c g
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  /// \ref amo93networkflows, \ref dantzig63linearprog,
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  /// \ref kellyoneill91netsimplex.
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  /// This algorithm is a highly efficient specialized version of the
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  /// linear programming simplex method directly for the minimum cost
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  /// flow problem.
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  ///
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  /// In general, %NetworkSimplex is the fastest implementation available
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  /// in LEMON for this problem.
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  /// Moreover, it supports both directions of the supply/demand inequality
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  /// constraints. For more information, see \ref SupplyType.
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  /// In general, \ref NetworkSimplex and \ref CostScaling are the fastest
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  /// implementations available in LEMON for this problem.
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  /// Furthermore, this class supports both directions of the supply/demand
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  /// inequality constraints. For more information, see \ref SupplyType.
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  ///
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  /// Most of the parameters of the problem (except for the digraph)
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  /// can be given using separate functions, and the algorithm can be
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  /// executed using the \ref run() function. If some parameters are not
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  /// specified, then default values will be used.
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  ///
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    /// the \ref run() function.
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    ///
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    /// \ref NetworkSimplex provides five different pivot rule
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    /// implementations that significantly affect the running time
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    /// of the algorithm.
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    /// By default, \ref BLOCK_SEARCH "Block Search" is used, which
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    /// proved to be the most efficient and the most robust on various
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    /// turend out to be the most efficient and the most robust on various
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    /// test inputs.
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    /// However, another pivot rule can be selected using the \ref run()
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    /// function with the proper parameter.
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    enum PivotRule {
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      /// The \e First \e Eligible pivot rule.
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    TEMPLATE_DIGRAPH_TYPEDEFS(GR);
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    typedef std::vector<int> IntVector;
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    typedef std::vector<Value> ValueVector;
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    typedef std::vector<Cost> CostVector;
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    typedef std::vector<signed char> CharVector;
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    // Note: vector<signed char> is used instead of vector<ArcState> and 
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    // Note: vector<signed char> is used instead of vector<ArcState> and
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    // vector<ArcDirection> for efficiency reasons
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    // State constants for arcs
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    enum ArcState {
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      STATE_UPPER = -1,
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      STATE_TREE  =  0,
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    ///
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    /// \param map A node map storing the supply values.
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    /// Its \c Value type must be convertible to the \c Value type
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    /// of the algorithm.
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    ///
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    /// \return <tt>(*this)</tt>
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    ///
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    /// \sa supplyType()
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    template<typename SupplyMap>
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    NetworkSimplex& supplyMap(const SupplyMap& map) {
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      for (NodeIt n(_graph); n != INVALID; ++n) {
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        _supply[_node_id[n]] = map[n];
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      }
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      return *this;
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    /// This function sets a single source node and a single target node
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    /// and the required flow value.
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    /// If neither this function nor \ref supplyMap() is used before
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    /// calling \ref run(), the supply of each node will be set to zero.
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    ///
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    /// Using this function has the same effect as using \ref supplyMap()
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    /// with such a map in which \c k is assigned to \c s, \c -k is
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    /// with a map in which \c k is assigned to \c s, \c -k is
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    /// assigned to \c t and all other nodes have zero supply value.
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    ///
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    /// \param s The source node.
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    /// \param t The target node.
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    /// \param k The required amount of flow from node \c s to node \c t
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    /// (i.e. the supply of \c s and the demand of \c t).
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