Module: ppl/ppl Branch: pip Commit: f9022a7ee764af85090f790bb738641c58dc9249 URL: http://www.cs.unipr.it/git/gitweb.cgi?p=ppl/ppl.git;a=commit;h=f9022a7ee764a...
Author: François Galea francois.galea@uvsq.fr Date: Fri Nov 27 16:39:59 2009 +0100
Modified the documentation of the PIP_Problem class.
---
src/PIP_Problem.defs.hh | 153 ++++++++++++++++++++++++++++++++++++++++++---- 1 files changed, 139 insertions(+), 14 deletions(-)
diff --git a/src/PIP_Problem.defs.hh b/src/PIP_Problem.defs.hh index 3668569..d080e3c 100644 --- a/src/PIP_Problem.defs.hh +++ b/src/PIP_Problem.defs.hh @@ -54,34 +54,159 @@ operator<<(std::ostream& s, const PIP_Problem& p); programming problem. The PIP problem is specified by providing: - the dimension of the vector space; - - the feasible region, by means of a finite set of linear equality - and (strict or non-strict) inequality constraints; - the subset of those dimensions of the vector space that are interpreted as integer parameters (the other space dimensions are interpreted as non-parameter integer variables); + - the feasible region, by means of a finite set of linear equality + and (strict or non-strict) inequality constraints involving both + variables and parameters; + - optionally, an initial context restricting the definition domain for + the parameters, by means of a finite set of linear equality and + (strict or non-strict) inequality constraints involving only + parameters; + - optionally, the dimension of a parameter to be considered arbitrarily + big; such a parameter is called the “big parameter”.
- // FIXME: should the non-negativity constraints be assumed? - // If that is the case, the problem will always be either - // unfeasible or optimizable (since the origin of the vector - // space is the minimum for the unconstrained non-negative problem). - - - the optimization mode (either maximization or minimization). + All variables and parameters are considered non-negative integers.
The class provides support for the (incremental) solution of the PIP problem based on variations of the revised simplex method and - on branch-and-bound techniques. The result of the resolution - process is expressed in terms of an enumeration, encoding the - feasibility and the unboundedness of the optimization problem. - The class supports simple feasibility tests (i.e., no optimization), - as well as the extraction of an optimal (resp., feasible) point, - provided the PIP_Problem is optimizable (resp., feasible). + on Gomory cut generation techniques. + + The solution for a PIP problem is the lexicographic minimum of the + integer points of the feasible region, expressed in terms of the + parameters. As the problem to be solved only involves non-negative + variables and parameters, the problem will always be either unfeasible + or optimizable. + + As the feasibility and the solution value of a PIP problem depend on the + values of the parameters, the solution is a binary decision tree, + dividing the context parameter set into subsets. The tree nodes are: + - decision nodes, encoding one or more linear tests on the parameters; + if all the tests are true, then the solution is the node's + “true” child, otherwise the solution is the node's + “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. + + Concerning the decision nodes, it may happen that a decision node has no + “false” or “true” child. This means the + corresponding test leads to an unfeasible solution. In addition, decision + nodes with two or more linear tests cannot have a “false” + child. + + Both tree node types may also contain the definition of extra parameters + which are artificially introduced by the solver to keep the solution + values integer. Such artificial parameters are defined by the integer + division of a linear expression on the parameters by an integer + coefficient.
By exploiting the incremental nature of the solver, it is possible to reuse part of the computational work already done when solving variants of a given PIP_Problem: currently, incremental resolution supports the addition of space dimensions, the addition of constraints, the change of objective function and the change of optimization mode. + + \par Example problem + An example PIP problem can be defined the following: + \code + 3*j >= -2*i+8 + j <= 4*i - 4 + i <= n + j <= m + \endcode + where \c i and \c j are variables, and \c n and \c m are parameters. + The resulting solution tree is: + \code + if 7*n >= 10 then + if 7*m >= 12 then + {i = 2 ; j = 2} + else + New Parameter P = (m) div 2 + if 2*n + 3*m >= 8 then + {i = -m - P + 4 ; j = m} + else + _|_ + else + _|_ + \endcode + The \f$\perp\f$ notation, also called “bottom”, denotes + the lexicographic minimum of an empty set, here meaning the problem is + infeasible.\n + Also notice that a new parameter is defined after the first \c else. It + is only valid inside the block where it is defined, and is used when + formulating the value of the <tt>{i,j}</tt> vector. + + \par Context restriction + The above solution is correct in an unrestricted original context, + meaning all possible values are allowed for the parameters. If we + restrict the context with the following parameter inequalities: + \code + m >= n + n >= 5 + \endcode + then the result will simply be: + \code + {i = 2 ; j = 2} + \endcode + + \par Problem object creation + The PIP_Problem object correspondind to the above example problem can be + created like follows: + \code + Variable i(0); + Variable j(1); + Variable n(2); + Variable m(3); + Variables_Set params(n, m); + Constraint_System cs; + cs.insert(3*j >= -2*i+8); + cs.insert(j <= 4*i - 4); + cs.insert(j <= m); + cs.insert(i <= n); + PIP_Problem pip(cs.space_dimension(), cs.begin(), cs.end(), params); + \endcode + If you want to restrict the original context, simply add the parameter + constraints the same way as for normal constraints. + \code + cs.insert(m >= n); + cs.insert(n >= 5); + \endcode + + \par Problem solving + Once a PIP_Problem object has been created, you can start the + resolution of the problem by calling the solve() method: + \code + PIP_Problem_Status status; + status = pip.solve(); + \endcode + where the returned \c status indicates if the problem has been optimized + or if it is unfeasible for any possible configuration of the parameter + values. + + \par Solution tree spanning + Retrieve the optimized 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: + \code + const PIP_Solution_Node* sol = node->as_solution(); + 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. */ + class Parma_Polyhedra_Library::PIP_Problem { friend class PIP_Solution_Node; public: