Enea Zaffanella wrote:

CVSROOT: /cvs/ppl Module name: ppl Changes by: zaffanella@cs.unipr.it 2006-09-21 10:05:24

Modified files: src : LP_Problem.defs.hh

Log message: Do not let the user assume that we solve the LP Problem by using a fixed algorithm (future releases may use a different one).

Fix some typos in that file.

Sebastian

Index: LP_Problem.defs.hh =================================================================== RCS file: /cvs/ppl/ppl/src/LP_Problem.defs.hh,v retrieving revision 1.21 diff -d -u -p -r1.21 LP_Problem.defs.hh --- LP_Problem.defs.hh 21 Sep 2006 09:17:11 -0000 1.21 +++ LP_Problem.defs.hh 21 Sep 2006 11:26:16 -0000 @@ -56,7 +56,7 @@ operator<<(std::ostream& s, const LP_Pro - the feasible region, by means of a finite set of linear equality and non-strict inequality constraints; - the objective function, described by a Linear_Expression; - - the oprimization mode (either maximization or minimization). + - the optimization mode (either maximization or minimization).

The class provides support for the (incremental) solution of the LP problem based on the simplex method. The result of the resolution @@ -81,7 +81,7 @@ public: the origin of the vector space is an optimal solution.

\param dim - The dimension of the vector space encosing \p *this + The dimension of the vector space enclosing \p *this (optional argument with default value \f$0\f$).

\exception std::length_error @@ -177,7 +177,7 @@ private: public: /*! \brief A type alias for the read-only iterator on the constraints - defining the feasible reagion. + defining the feasible region. */ typedef Constraint_Sequence::const_iterator const_iterator;

@@ -465,7 +465,7 @@ private:

\param unfeasible_tableau_rows This will contain all the row indexes of the tableau that are no more - satisfied after adding more contraints to \p *this. + satisfied after adding more constraints to \p *this.

\param satisfied_ineqs This will contain all the row indexes of the tableau that are already @@ -486,7 +486,7 @@ private:

\return The column index of the variable that enters the base. If no such - variable exists, optimality was achieved and <CODE>0</CODE> is retuned. + variable exists, optimality was achieved and <CODE>0</CODE> is returned. */ dimension_type textbook_entering_index() const;

@@ -538,7 +538,7 @@ private:

\return The column index of the variable that enters the base. If no such - variable exists, optimality was achieved and <CODE>0</CODE> is retuned. + variable exists, optimality was achieved and <CODE>0</CODE> is returned.

To compute the entering_index, the steepest edge algorithm chooses the index `j' such that \f$\frac{d_{j}}{|\Delta x^{j} |}\f$ is the @@ -551,7 +551,7 @@ private: \f] Recall that, due to the Integer implementation of the algorithm, our tableau doesn't contain the ``real'' \f$\alpha\f$ values, but these - can be computed dividing the value of the cofficient by the value of + can be computed dividing the value of the coefficient by the value of the variable in base. Obviously the result may not be an Integer, so we will proceed in another way: the following code will compute the lcm of all the variables in base to get the good ``weight'' of each @@ -576,7 +576,7 @@ private: dimension_type& row_index) const;

/*! \brief - Computes a valid generator that satisifies all the constraints of the + Computes a valid generator that satisfies all the constraints of the Linear Programming problem associated to \p *this. */ void compute_generator() const;