Module: ppl/ppl Branch: pip Commit: 4394384f20722ac901f4b934780238580eae1767 URL: http://www.cs.unipr.it/git/gitweb.cgi?p=ppl/ppl.git;a=commit;h=4394384f20722...
Author: François Galea francois.galea@uvsq.fr Date: Mon Nov 30 17:08:59 2009 +0100
Improved the documentation of the PIP solver.
---
src/PIP_Problem.defs.hh | 86 +++++++++++++++++++++++++++++++++++++++++------ 1 files changed, 75 insertions(+), 11 deletions(-)
diff --git a/src/PIP_Problem.defs.hh b/src/PIP_Problem.defs.hh index d080e3c..e6768bb 100644 --- a/src/PIP_Problem.defs.hh +++ b/src/PIP_Problem.defs.hh @@ -88,7 +88,10 @@ operator<<(std::ostream& s, const PIP_Problem& p); “false” child; - solution nodes, encoding the solution of the problem in the current context subset, where each variable is defined in terms of a linear - expression of the parameters. + expression of the parameters. Solution nodes also embed an optionally + not empty set of parameter constraints, meaning if all constraint tests + are true, the solution is described by the node, otherwise the problem + is empty.
Concerning the decision nodes, it may happen that a decision node has no “false” or “true” child. This means the @@ -186,25 +189,86 @@ operator<<(std::ostream& s, const PIP_Problem& p); values.
\par Solution tree spanning - Retrieve the optimized decision tree: + Retrieve the optimized solution decision tree: \code const PIP_Tree_Node* node = solution(); \endcode If the pointer designates a \f$\perp\f$ solution (infeasible), its value is \c 0.\n - FIXME: insert_artificials() - To check whether a non-null tree node pointer designates a solution or a - decision node, you can call the as_decision() and as_solution() virtual - methods: + decision node, you can call the PIP_Tree_Node::as_decision() and + PIP_Tree_Node::as_solution() virtual methods: \code const PIP_Solution_Node* sol = node->as_solution(); - const PIP_Decision_Node* dec = node->as_decision(); + if (sol != 0) { + // The node is a solution node + ... + } + else { + // The node is a decision node + const PIP_Decision_Node* dec = node->as_decision(); + ... + } \endcode - Exactly one of the \c sol and \c dec pointers will be \c 0. Then the - node type is of the kind of the non-null pointer.\n - FIXME: list of decision node tests, get true and false children - FIXME: list expressions of variables. + + \par + To access the two child nodes of a Decision Node, use the + PIP_Tree_Node::child_node(bool) method, using \b true or \b false as an + argument to access the child node you want. + + \par Artificial parameters + The expression of an artificial parameter is stored in an + PIP_Tree_Node::Artificial_Parameter object, which encodes the integer + division of a Linear_Expression (only expressed in terms of the other + parameters, including the previously-defined artificials) by a + denominator Coefficient. + To get the effective value of an artificial parameter, just divide the + result of the Linear_Expression applied to the effective values of the + other parameters by the value of the denominator. + The dimensions of the artificial parameters in a node can be computed as + <tt>dim</tt>, <tt>dim+1</tt>, ... where <tt>dim</tt>'s value is computed + the following way: + - for the root node \c dim is the maximum space dimension of the + original problem, plus one. + - for any other node of the tree, it is the sum of the \c dim value + computed for the parent node, plus the total number of artificial + parameters in the parent node. + \par + The definition of artificial parameters' dimension values always follow + this rule. If you choose to add a new variable or parameter on a problem + which already has a solution (incremental solving), all artificial + parameters in the solution will have their dimension values incremented. + + \par Node constraints + All node types can embed parameter-only constraints. Decision nodes + contain at least one of them. You can access the node's constraints set + using the PIP_Tree_Node::constraints() method. + The signification of the node constraints is the following: + - On a decision node, if all tests in the constraints are true, then the + solution is the “true” child; otherwise it is the + “false” child. If the number of constraints is greater than + one, the “false” child is always infeasible. + - On a solution node, if the constraint set is empty or all tests in the + constraints are true, then the solution is described by the node; + otherwise the solution is \f$\perp\f$ (infeasible problem). + \par + Node constraints always are defined in terms of the parameters, including + those from the original problem and any of the artificial parameters + defined in nodes which belong to the path from the root to the concerned + node. + + \par Getting the optimal values for the variables + Once having spanned the solution tree using the actual values of the + parameters, and having ended at a solution node, you can fetch the + parametric expression of any of the variables using the + PIP_Solution_Node::parametric_values() method. + \par + Variable expressions always are defined in terms of the parameters, + including those from the original problem and any of the artificial + parameters defined in nodes which belong to the path from the root to the + concerned node. + + FIXME: different uses of the big parameter. */
class Parma_Polyhedra_Library::PIP_Problem {