pub fn standardize(&mut self) -> Result<(), LinearProgramType<OF>> {
        self.presolve()?;
        self.transform_variables();
        self.make_b_non_negative();
        self.make_minimization_problem();

        Ok(())
    }
/// Convert this `GeneralForm` problem to a form closer to the standard form representation.
///
/// This involves:
///
/// * Determining which rows and columns can be removed or differently represented to reduce the
/// problem size and increase the reading speed from a `MatrixData` structure.
/// * Determining which variables are implicitly fixed, such that their only feasible value can
/// be substituted into the problem and the column eliminated.
/// * Modifying variables such that they are either free or bounded below by zero (with possibly
/// an upper bound).
/// * Multiplying some rows such that the constraint value is non-negative.
///
/// To do the above, a column major representation of the constraint data is built. This
/// requires copying all constraint data once.
///
/// TODO(ENHANCEMENT): Make sure that presolving can be skipped.
///
/// # Errors
///
/// In case the linear program gets solved during this presolve operation, a solution.
Convert this `GeneralForm` problem to a form closer to the standard form representation.
This involves.

Determining which rows and columns can be removed or differently represented to reduce the
problem size and increase the reading speed from a `MatrixData` structure.
Determining which variables are implicitly fixed, such that their only feasible value can
be substituted into the problem and the column eliminated.
Modifying variables such that they are either free or bounded below by zero (with possibly
an upper bound).
Multiplying some rows such that the constraint value is non-negative.

To do the above, a column major representation of the constraint data is built. This
requires copying all constraint data once.

Make sure that presolving can be skipped.
Convert this ` GeneralForm ` problem to a form closer to the standard form representation . This involves . Determining which rows and columns can be removed or differently represented to reduce the problem size and increase the reading speed from a ` MatrixData ` structure . Determining which variables are implicitly fixed such that their only feasible value can be substituted into the problem and the column eliminated . Modifying variables such that they are either free or bounded below by zero ( with possibly an upper bound ) . Multiplying some rows such that the constraint value is non - negative . To do the above a column major representation of the constraint data is built . This requires copying all constraint data once . Make sure that presolving can be skipped .
pub fn standardize(&mut self) -> Result<(), LinearProgramType<OF>> {
        self.presolve()?;
        self.transform_variables();
        self.make_b_non_negative();
        self.make_minimization_problem();
        Ok(())
    }
pub fn standardize ( & mut self ) -> Result < ( ) , LinearProgramType < OF > > { self . presolve ( ) ? ; self . transform_variables ( ) ; self . make_b_non_negative ( ) ; self . make_minimization_problem ( ) ; Ok ( ( ) ) }
Convert this `GeneralForm` problem to a form closer to the standard form representation.
Convert this ` GeneralForm ` problem to a form closer to the standard form representation .
&mut self
&mut self
errors
In case the linear program gets solved during this presolve operation, a solution.
In case the linear program gets solved during this presolve operation a solution .