LEMON/LEMON-official Changeset - r843:189760a7cdd0

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Changeset - r843:189760a7cdd0

« 842:dc9316

844:c01a98 »

r843:189760a7cdd0

Thu, 07 May 2009 02:05:12

[Not Reviewed]

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kpeter (Peter Kovacs)
kpeter@inf.elte.hu

Remove references of missing tools (#257)

0 2 0

1.1

2 files changed with 18 insertions and 106 deletions:

doc/groups.dox

104

lemon/suurballe.h

↑ Collapse diff ↑

doc/groups.dox

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@@ -233,20 +233,12 @@
 		the two maps which can be done implicitly with the \c DivMap template
 		class. We use the implicit minimum time map as the length map of the
 		\c Dijkstra algorithm.
 		*/
 		/**
 		@defgroup matrices Matrices
 		@ingroup datas
 		\brief Two dimensional data storages implemented in LEMON.
 		This group contains two dimensional data storages implemented in LEMON.
 		*/
 		/**
 		@defgroup paths Path Structures
 		@ingroup datas
 		\brief %Path structures implemented in LEMON.
 		This group contains the path structures implemented in LEMON.
@@ -290,22 +282,14 @@
 		@defgroup shortest_path Shortest Path Algorithms
 		@ingroup algs
 		\brief Algorithms for finding shortest paths.
 		This group contains the algorithms for finding shortest paths in digraphs.
 		 - \ref Dijkstra algorithm for finding shortest paths from a source node
 		   when all arc lengths are non-negative.
 		 - \ref BellmanFord "Bellman-Ford" algorithm for finding shortest paths
 		   from a source node when arc lenghts can be either positive or negative,
 		   but the digraph should not contain directed cycles with negative total
 		   length.
 		 - \ref FloydWarshall "Floyd-Warshall" and \ref Johnson "Johnson" algorithms
 		   for solving the \e all-pairs \e shortest \e paths \e problem when arc
 		   lenghts can be either positive or negative, but the digraph should
 		   not contain directed cycles with negative total length.
 		 - \ref Dijkstra Dijkstra's algorithm for finding shortest paths from a
 		   source node when all arc lengths are non-negative.
 		 - \ref Suurballe A successive shortest path algorithm for finding
 		   arc-disjoint paths between two nodes having minimum total length.
 		*/
 		/**
 		@defgroup max_flow Maximum Flow Algorithms
@@ -324,22 +308,20 @@
 		\f[ \max\sum_{sv\in A} f(sv) - \sum_{vs\in A} f(vs) \f]
 		\f[ \sum_{uv\in A} f(uv) = \sum_{vu\in A} f(vu)
 		    \quad \forall u\in V\setminus\{s,t\} \f]
 		\f[ 0 \leq f(uv) \leq cap(uv) \quad \forall uv\in A \f]
 		LEMON contains several algorithms for solving maximum flow problems:
 		- \ref EdmondsKarp Edmonds-Karp algorithm.
 		- \ref Preflow Goldberg-Tarjan's preflow push-relabel algorithm.
 		- \ref DinitzSleatorTarjan Dinitz's blocking flow algorithm with dynamic trees.
-		- \ref GoldbergTarjan Preflow push-relabel algorithm with dynamic trees.
+		\ref Preflow implements the preflow push-relabel algorithm of Goldberg and
 		Tarjan for solving this problem. It also provides functions to query the
 		minimum cut, which is the dual problem of maximum flow.
 		In most cases the \ref Preflow "Preflow" algorithm provides the
 		fastest method for computing a maximum flow. All implementations
 		provides functions to also query the minimum cut, which is the dual
 		problem of the maximum flow.
 		\ref Circulation is a preflow push-relabel algorithm implemented directly
 		for finding feasible circulations, which is a somewhat different problem,
 		but it is strongly related to maximum flow.
 		For more information, see \ref Circulation.
 		*/
 		/**
 		@defgroup min_cost_flow Minimum Cost Flow Algorithms
 		@ingroup algs
@@ -418,33 +400,15 @@
 		     then \f$\pi(u)=0\f$.
 		Here \f$cost^\pi(uv)\f$ denotes the \e reduced \e cost of the arc
 		\f$uv\in A\f$ with respect to the potential function \f$\pi\f$, i.e.
 		\f[ cost^\pi(uv) = cost(uv) + \pi(u) - \pi(v).\f]
 		All algorithms provide dual solution (node potentials) as well,
 		if an optimal flow is found.
 		LEMON contains several algorithms for solving minimum cost flow problems.
 		 - \ref NetworkSimplex Primal Network Simplex algorithm with various
 		   pivot strategies.
 		 - \ref CostScaling Push-Relabel and Augment-Relabel algorithms based on
 		   cost scaling.
 		 - \ref CapacityScaling Successive Shortest %Path algorithm with optional
 		   capacity scaling.
 		 - \ref CancelAndTighten The Cancel and Tighten algorithm.
 		 - \ref CycleCanceling Cycle-Canceling algorithms.
 		Most of these implementations support the general inequality form of the
 		minimum cost flow problem, but CancelAndTighten and CycleCanceling
 		only support the equality form due to the primal method they use.
 		In general NetworkSimplex is the most efficient implementation,
 		but in special cases other algorithms could be faster.
 		For example, if the total supply and/or capacities are rather small,
-		CapacityScaling is usually the fastest algorithm (without effective scaling).
+		\ref NetworkSimplex is an efficient implementation of the primal Network
 		Simplex algorithm for finding minimum cost flows. It also provides dual
 		solution (node potentials), if an optimal flow is found.
 		*/
 		/**
 		@defgroup min_cut Minimum Cut Algorithms
 		@ingroup algs
@@ -462,14 +426,12 @@
 		    \sum_{uv\in A, u\in X, v\not\in X}cap(uv) \f]
 		LEMON contains several algorithms related to minimum cut problems:
 		- \ref HaoOrlin "Hao-Orlin algorithm" for calculating minimum cut
 		  in directed graphs.
 		- \ref NagamochiIbaraki "Nagamochi-Ibaraki algorithm" for
 		  calculating minimum cut in undirected graphs.
 		- \ref GomoryHu "Gomory-Hu tree computation" for calculating
 		  all-pairs minimum cut in undirected graphs.
 		If you want to find minimum cut just between two distinict nodes,
 		see the \ref max_flow "maximum flow problem".
 		*/
@@ -484,51 +446,27 @@
 		\image html edge_biconnected_components.png
 		\image latex edge_biconnected_components.eps "bi-edge-connected components" width=\textwidth
 		*/
 		/**
 		@defgroup planar Planarity Embedding and Drawing
 		@ingroup algs
 		\brief Algorithms for planarity checking, embedding and drawing
 		This group contains the algorithms for planarity checking,
 		embedding and drawing.
 		\image html planar.png
 		\image latex planar.eps "Plane graph" width=\textwidth
 		*/
 		/**
 		@defgroup matching Matching Algorithms
 		@ingroup algs
 		\brief Algorithms for finding matchings in graphs and bipartite graphs.
 		This group contains the algorithms for calculating
 		matchings in graphs and bipartite graphs. The general matching problem is
 		finding a subset of the edges for which each node has at most one incident
 		edge.
 		This group contains the algorithms for calculating matchings in graphs.
 		The general matching problem is finding a subset of the edges for which
 		each node has at most one incident edge.
 		There are several different algorithms for calculate matchings in
 		graphs.  The matching problems in bipartite graphs are generally
 		easier than in general graphs. The goal of the matching optimization
 		graphs. The goal of the matching optimization
 		can be finding maximum cardinality, maximum weight or minimum cost
 		matching. The search can be constrained to find perfect or
 		maximum cardinality matching.
 		The matching algorithms implemented in LEMON:
 		- \ref MaxBipartiteMatching Hopcroft-Karp augmenting path algorithm
 		  for calculating maximum cardinality matching in bipartite graphs.
 		- \ref PrBipartiteMatching Push-relabel algorithm
 		  for calculating maximum cardinality matching in bipartite graphs.
 		- \ref MaxWeightedBipartiteMatching
 		  Successive shortest path algorithm for calculating maximum weighted
 		  matching and maximum weighted bipartite matching in bipartite graphs.
 		- \ref MinCostMaxBipartiteMatching
 		  Successive shortest path algorithm for calculating minimum cost maximum
 		  matching in bipartite graphs.
 		- \ref MaxMatching Edmond's blossom shrinking algorithm for calculating
 		  maximum cardinality matching in general graphs.
 		- \ref MaxWeightedMatching Edmond's blossom shrinking algorithm for calculating
 		  maximum weighted matching in general graphs.
 		- \ref MaxWeightedPerfectMatching
 		  Edmond's blossom shrinking algorithm for calculating maximum weighted
@@ -554,21 +492,12 @@
 		This group contains some algorithms implemented in LEMON
 		in order to make it easier to implement complex algorithms.
 		*/
 		/**
 		@defgroup approx Approximation Algorithms
 		@ingroup algs
 		\brief Approximation algorithms.
 		This group contains the approximation and heuristic algorithms
 		implemented in LEMON.
 		*/
 		/**
 		@defgroup gen_opt_group General Optimization Tools
 		\brief This group contains some general optimization frameworks
 		implemented in LEMON.
 		This group contains some general optimization frameworks
 		implemented in LEMON.
@@ -582,29 +511,12 @@
 		This group contains Lp and Mip solver interfaces for LEMON. The
 		various LP solvers could be used in the same manner with this
 		interface.
 		*/
 		/**
 		@defgroup lp_utils Tools for Lp and Mip Solvers
 		@ingroup lp_group
 		\brief Helper tools to the Lp and Mip solvers.
 		This group adds some helper tools to general optimization framework
 		implemented in LEMON.
 		*/
 		/**
 		@defgroup metah Metaheuristics
 		@ingroup gen_opt_group
 		\brief Metaheuristics for LEMON library.
 		This group contains some metaheuristic optimization tools.
 		*/
 		/**
 		@defgroup utils Tools and Utilities
 		\brief Tools and utilities for programming in LEMON
 		Tools and utilities for programming in LEMON.
 		*/

lemon/suurballe.h

Show inline comments

@@ -42,14 +42,14 @@
 		  /// \ref lemon::Suurballe "Suurballe" implements an algorithm for
 		  /// finding arc-disjoint paths having minimum total length (cost)
 		  /// from a given source node to a given target node in a digraph.
 		  ///
 		  /// Note that this problem is a special case of the \ref min_cost_flow
 		  /// "minimum cost flow problem". This implementation is actually an
 		  /// efficient specialized version of the \ref CapacityScaling
 		  /// "Successive Shortest Path" algorithm directly for this problem.
 		  /// efficient specialized version of the Successive Shortest Path
 		  /// algorithm directly for this problem.
 		  /// Therefore this class provides query functions for flow values and
 		  /// node potentials (the dual solution) just like the minimum cost flow
 		  /// algorithms.
 		  ///
 		  /// \tparam GR The digraph type the algorithm runs on.
 		  /// \tparam LEN The type of the length map.

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