diff options
Diffstat (limited to 'eigen/Eigen/src/Cholesky/LDLT.h')
| -rw-r--r-- | eigen/Eigen/src/Cholesky/LDLT.h | 11 |
1 files changed, 7 insertions, 4 deletions
diff --git a/eigen/Eigen/src/Cholesky/LDLT.h b/eigen/Eigen/src/Cholesky/LDLT.h index fcee7b2..0313a54 100644 --- a/eigen/Eigen/src/Cholesky/LDLT.h +++ b/eigen/Eigen/src/Cholesky/LDLT.h @@ -248,7 +248,7 @@ template<typename _MatrixType, int _UpLo> class LDLT /** \brief Reports whether previous computation was successful. * * \returns \c Success if computation was succesful, - * \c NumericalIssue if the matrix.appears to be negative. + * \c NumericalIssue if the factorization failed because of a zero pivot. */ ComputationInfo info() const { @@ -376,6 +376,8 @@ template<> struct ldlt_inplace<Lower> if((rs>0) && pivot_is_valid) A21 /= realAkk; + else if(rs>0) + ret = ret && (A21.array()==Scalar(0)).all(); if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed else if(!pivot_is_valid) found_zero_pivot = true; @@ -568,13 +570,14 @@ void LDLT<_MatrixType,_UpLo>::_solve_impl(const RhsType &rhs, DstType &dst) cons // more precisely, use pseudo-inverse of D (see bug 241) using std::abs; const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD()); - // In some previous versions, tolerance was set to the max of 1/highest and the maximal diagonal entry * epsilon - // as motivated by LAPACK's xGELSS: + // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min()) + // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS: // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest()); // However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest // diagonal element is not well justified and leads to numerical issues in some cases. // Moreover, Lapack's xSYTRS routines use 0 for the tolerance. - RealScalar tolerance = RealScalar(1) / NumTraits<RealScalar>::highest(); + // Using numeric_limits::min() gives us more robustness to denormals. + RealScalar tolerance = (std::numeric_limits<RealScalar>::min)(); for (Index i = 0; i < vecD.size(); ++i) { |