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Added support for custom-scalars

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Pavel Holoborodko 2013-09-02 19:09:39 +09:00
parent ace2ed7b87
commit ebd6a7a46c
1 changed files with 5 additions and 4 deletions
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9
Eigen/src/SparseQR/SparseQR.h
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@ -205,7 +205,7 @@ class SparseQR
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \returns \c Success if computation was successful,
* \c NumericalIssue if the QR factorization reports a numerical problem
* \c InvalidInput if the input matrix is invalid
*
@ -256,7 +256,7 @@ class SparseQR
/** \brief Preprocessing step of a QR factorization
*
* In this step, the fill-reducing permutation is computed and applied to the columns of A
* and the column elimination tree is computed as well. Only the sparcity pattern of \a mat is exploited.
* and the column elimination tree is computed as well. Only the sparsity pattern of \a mat is exploited.
*
* \note In this step it is assumed that there is no empty row in the matrix \a mat.
*/
@ -292,7 +292,7 @@ void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat)
/** \brief Performs the numerical QR factorization of the input matrix
*
* The function SparseQR::analyzePattern(const MatrixType&) must have been called beforehand with
* a matrix having the same sparcity pattern than \a mat.
* a matrix having the same sparsity pattern than \a mat.
*
* \param mat The sparse column-major matrix
*/
@ -445,7 +445,8 @@ void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
}
else
{
beta = std::sqrt(numext::abs2(c0) + sqrNorm);
using std::sqrt;
beta = sqrt(numext::abs2(c0) + sqrNorm);
if(numext::real(c0) >= RealScalar(0))
beta = -beta;
tval(Qidx(0)) = 1;