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JacobiSVD: move from Lapack to Matlab strategy for the default threshold

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(grafted from 019dcfc21d
)
This commit is contained in:
Gael Guennebaud
2013-11-03 13:18:56 +01:00
parent e16e52d493
commit 0c4fc69d62
2 changed files with 59 additions and 6 deletions
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12
Eigen/src/SVD/JacobiSVD.h
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@@ -533,7 +533,7 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
m_isAllocated(false),
m_usePrescribedThreshold(false),
m_computationOptions(0),
m_rows(-1), m_cols(-1)
m_rows(-1), m_cols(-1), m_diagSize(0)
{}
@@ -729,7 +729,7 @@ template<typename _MatrixType, int QRPreconditioner> class JacobiSVD
{
eigen_assert(m_isInitialized || m_usePrescribedThreshold);
return m_usePrescribedThreshold ? m_prescribedThreshold
: NumTraits<Scalar>::epsilon();
: (std::max<Index>)(1,m_diagSize)*NumTraits<Scalar>::epsilon();
}
inline Index rows() const { return m_rows; }
@@ -921,11 +921,11 @@ struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
// So A^{-1} = V S^{-1} U^*
Matrix<Scalar, Dynamic, Rhs::ColsAtCompileTime, 0, _MatrixType::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime> tmp;
Index nonzeroSingVals = dec().rank();
Index rank = dec().rank();
tmp.noalias() = dec().matrixU().leftCols(nonzeroSingVals).adjoint() * rhs();
tmp = dec().singularValues().head(nonzeroSingVals).asDiagonal().inverse() * tmp;
dst = dec().matrixV().leftCols(nonzeroSingVals) * tmp;
tmp.noalias() = dec().matrixU().leftCols(rank).adjoint() * rhs();
tmp = dec().singularValues().head(rank).asDiagonal().inverse() * tmp;
dst = dec().matrixV().leftCols(rank) * tmp;
}
};
} // end namespace internal