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set the default number of iteration to the size of the problem

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This commit is contained in:
Gael Guennebaud 2011-12-27 16:38:05 +01:00
parent 15ea999f84
commit bdee0c9baa
3 changed files with 14 additions and 11 deletions
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6
Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
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@ -125,8 +125,8 @@ struct traits<BiCGSTAB<_MatrixType,_Preconditioner> >
* \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
*
* The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
* and setTolerance() methods. The default are 1000 max iterations and NumTraits<Scalar>::epsilon()
* for the tolerance.
* and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
* and NumTraits<Scalar>::epsilon() for the tolerance.
*
* This class can be used as the direct solver classes. Here is a typical usage example:
* \code
@ -218,7 +218,7 @@ public:
{
for(int j=0; j<b.cols(); ++j)
{
m_iterations = Base::m_maxIterations;
m_iterations = Base::maxIterations();
m_error = Base::m_tolerance;
typename Dest::ColXpr xj(x,j);

8
Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
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@ -111,8 +111,8 @@ struct traits<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> >
* \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner
*
* The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
* and setTolerance() methods. The default are 1000 max iterations and NumTraits<Scalar>::epsilon()
* for the tolerance.
* and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
* and NumTraits<Scalar>::epsilon() for the tolerance.
*
* This class can be used as the direct solver classes. Here is a typical usage example:
* \code
@ -206,12 +206,12 @@ public:
template<typename Rhs,typename Dest>
void _solveWithGuess(const Rhs& b, Dest& x) const
{
m_iterations = Base::m_maxIterations;
m_iterations = Base::maxIterations();
m_error = Base::m_tolerance;
for(int j=0; j<b.cols(); ++j)
{
m_iterations = Base::m_maxIterations;
m_iterations = Base::maxIterations();
m_error = Base::m_tolerance;
typename Dest::ColXpr xj(x,j);

11
Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
View File

@ -91,9 +91,9 @@ public:
}
/** \internal */
Index rows() const { return mp_matrix->rows(); }
Index rows() const { return mp_matrix ? mp_matrix->rows() : 0; }
/** \internal */
Index cols() const { return mp_matrix->cols(); }
Index cols() const { return mp_matrix ? mp_matrix->cols() : 0; }
/** \returns the tolerance threshold used by the stopping criteria */
RealScalar tolerance() const { return m_tolerance; }
@ -112,7 +112,10 @@ public:
const Preconditioner& preconditioner() const { return m_preconditioner; }
/** \returns the max number of iterations */
int maxIterations() const { return m_maxIterations; }
int maxIterations() const
{
return (mp_matrix && m_maxIterations<0) ? mp_matrix->cols() : m_maxIterations;
}
/** Sets the max number of iterations */
Derived& setMaxIterations(int maxIters)
@ -191,7 +194,7 @@ protected:
void init()
{
m_isInitialized = false;
m_maxIterations = 1000;
m_maxIterations = -1;
m_tolerance = NumTraits<Scalar>::epsilon();
}
const MatrixType* mp_matrix;