diff -r 189760a7cdd0 -r c01a98ce01fd doc/groups.dox --- a/doc/groups.dox Thu May 07 02:05:12 2009 +0200 +++ b/doc/groups.dox Tue May 12 12:09:55 2009 +0100 @@ -138,16 +138,6 @@ */ /** -@defgroup semi_adaptors Semi-Adaptor Classes for Graphs -@ingroup graphs -\brief Graph types between real graphs and graph adaptors. - -This group contains some graph types between real graphs and graph adaptors. -These classes wrap graphs to give new functionality as the adaptors do it. -On the other hand they are not light-weight structures as the adaptors. -*/ - -/** @defgroup maps Maps @ingroup datas \brief Map structures implemented in LEMON. @@ -315,6 +305,7 @@ Tarjan for solving this problem. It also provides functions to query the minimum cut, which is the dual problem of 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. @@ -322,86 +313,14 @@ */ /** -@defgroup min_cost_flow Minimum Cost Flow Algorithms +@defgroup min_cost_flow_algs Minimum Cost Flow Algorithms @ingroup algs \brief Algorithms for finding minimum cost flows and circulations. This group contains the algorithms for finding minimum cost flows and -circulations. - -The \e minimum \e cost \e flow \e problem is to find a feasible flow of -minimum total cost from a set of supply nodes to a set of demand nodes -in a network with capacity constraints (lower and upper bounds) -and arc costs. -Formally, let \f$G=(V,A)\f$ be a digraph, \f$lower: A\rightarrow\mathbf{Z}\f$, -\f$upper: A\rightarrow\mathbf{Z}\cup\{+\infty\}\f$ denote the lower and -upper bounds for the flow values on the arcs, for which -\f$lower(uv) \leq upper(uv)\f$ must hold for all \f$uv\in A\f$, -\f$cost: A\rightarrow\mathbf{Z}\f$ denotes the cost per unit flow -on the arcs and \f$sup: V\rightarrow\mathbf{Z}\f$ denotes the -signed supply values of the nodes. -If \f$sup(u)>0\f$, then \f$u\f$ is a supply node with \f$sup(u)\f$ -supply, if \f$sup(u)<0\f$, then \f$u\f$ is a demand node with -\f$-sup(u)\f$ demand. -A minimum cost flow is an \f$f: A\rightarrow\mathbf{Z}\f$ solution -of the following optimization problem. - -\f[ \min\sum_{uv\in A} f(uv) \cdot cost(uv) \f] -\f[ \sum_{uv\in A} f(uv) - \sum_{vu\in A} f(vu) \geq - sup(u) \quad \forall u\in V \f] -\f[ lower(uv) \leq f(uv) \leq upper(uv) \quad \forall uv\in A \f] - -The sum of the supply values, i.e. \f$\sum_{u\in V} sup(u)\f$ must be -zero or negative in order to have a feasible solution (since the sum -of the expressions on the left-hand side of the inequalities is zero). -It means that the total demand must be greater or equal to the total -supply and all the supplies have to be carried out from the supply nodes, -but there could be demands that are not satisfied. -If \f$\sum_{u\in V} sup(u)\f$ is zero, then all the supply/demand -constraints have to be satisfied with equality, i.e. all demands -have to be satisfied and all supplies have to be used. - -If you need the opposite inequalities in the supply/demand constraints -(i.e. the total demand is less than the total supply and all the demands -have to be satisfied while there could be supplies that are not used), -then you could easily transform the problem to the above form by reversing -the direction of the arcs and taking the negative of the supply values -(e.g. using \ref ReverseDigraph and \ref NegMap adaptors). -However \ref NetworkSimplex algorithm also supports this form directly -for the sake of convenience. - -A feasible solution for this problem can be found using \ref Circulation. - -Note that the above formulation is actually more general than the usual -definition of the minimum cost flow problem, in which strict equalities -are required in the supply/demand contraints, i.e. - -\f[ \sum_{uv\in A} f(uv) - \sum_{vu\in A} f(vu) = - sup(u) \quad \forall u\in V. \f] - -However if the sum of the supply values is zero, then these two problems -are equivalent. So if you need the equality form, you have to ensure this -additional contraint for the algorithms. - -The dual solution of the minimum cost flow problem is represented by node -potentials \f$\pi: V\rightarrow\mathbf{Z}\f$. -An \f$f: A\rightarrow\mathbf{Z}\f$ feasible solution of the problem -is optimal if and only if for some \f$\pi: V\rightarrow\mathbf{Z}\f$ -node potentials the following \e complementary \e slackness optimality -conditions hold. - - - For all \f$uv\in A\f$ arcs: - - if \f$cost^\pi(uv)>0\f$, then \f$f(uv)=lower(uv)\f$; - - if \f$lower(uv)