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@@ -196,7 +196,7 @@ template<typename _MatrixType, int _UpLo> class LDLT
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LDLT& compute(const MatrixType& matrix);
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template <typename Derived>
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LDLT& rankUpdate(const MatrixBase<Derived>& w,RealScalar alpha=1);
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LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
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/** \returns the internal LDLT decomposition matrix
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*
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@@ -347,7 +347,7 @@ template<> struct ldlt_inplace<Lower>
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// Here only rank-1 updates are implemented, to reduce the
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// requirement for intermediate storage and improve accuracy
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template<typename MatrixType, typename WDerived>
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static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, typename MatrixType::RealScalar sigma=1)
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static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1)
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{
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using internal::isfinite;
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typedef typename MatrixType::Scalar Scalar;
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@@ -386,7 +386,7 @@ template<> struct ldlt_inplace<Lower>
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}
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template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
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static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, typename MatrixType::RealScalar sigma=1)
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static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1)
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{
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// Apply the permutation to the input w
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tmp = transpositions * w;
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@@ -405,7 +405,7 @@ template<> struct ldlt_inplace<Upper>
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}
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template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
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static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, typename MatrixType::RealScalar sigma=1)
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static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1)
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{
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Transpose<MatrixType> matt(mat);
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return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
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@@ -457,7 +457,7 @@ LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
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*/
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template<typename MatrixType, int _UpLo>
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template<typename Derived>
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LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w,typename NumTraits<typename MatrixType::Scalar>::Real sigma)
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LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename NumTraits<typename MatrixType::Scalar>::Real& sigma)
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{
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const Index size = w.rows();
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if (m_isInitialized)
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@@ -200,7 +200,7 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
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typedef Matrix<Scalar,Dynamic,1> TempVectorType;
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typedef typename TempVectorType::SegmentReturnType TempVecSegment;
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int n = mat.cols();
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Index n = mat.cols();
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eigen_assert(mat.rows()==n && vec.size()==n);
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TempVectorType temp;
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@@ -212,12 +212,12 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
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// i.e., for sigma > 0
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temp = sqrt(sigma) * vec;
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for(int i=0; i<n; ++i)
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for(Index i=0; i<n; ++i)
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{
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JacobiRotation<Scalar> g;
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g.makeGivens(mat(i,i), -temp(i), &mat(i,i));
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int rs = n-i-1;
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Index rs = n-i-1;
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if(rs>0)
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{
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ColXprSegment x(mat.col(i).tail(rs));
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@@ -230,7 +230,7 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
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{
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temp = vec;
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RealScalar beta = 1;
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for(int j=0; j<n; ++j)
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for(Index j=0; j<n; ++j)
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{
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RealScalar Ljj = real(mat.coeff(j,j));
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RealScalar dj = abs2(Ljj);
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