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@@ -259,7 +259,7 @@ template<> struct ldlt_inplace<Lower>
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{
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transpositions.setIdentity();
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if(sign)
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*sign = real(mat.coeff(0,0))>0 ? 1:-1;
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*sign = numext::real(mat.coeff(0,0))>0 ? 1:-1;
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return true;
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}
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@@ -278,22 +278,13 @@ template<> struct ldlt_inplace<Lower>
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// are compared; if any diagonal is negligible compared
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// to the largest overall, the algorithm bails.
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cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
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if(sign)
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*sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
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}
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else if(sign)
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{
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// LDLT is not guaranteed to work for indefinite matrices, but let's try to get the sign right
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int newSign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0;
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if(newSign != *sign)
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*sign = 0;
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}
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// Finish early if the matrix is not full rank.
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if(biggest_in_corner < cutoff)
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{
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for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i;
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if(sign) *sign = 0;
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break;
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}
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@@ -309,11 +300,11 @@ template<> struct ldlt_inplace<Lower>
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for(int i=k+1;i<index_of_biggest_in_corner;++i)
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{
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Scalar tmp = mat.coeffRef(i,k);
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mat.coeffRef(i,k) = conj(mat.coeffRef(index_of_biggest_in_corner,i));
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mat.coeffRef(index_of_biggest_in_corner,i) = conj(tmp);
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mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
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mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp);
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}
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if(NumTraits<Scalar>::IsComplex)
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mat.coeffRef(index_of_biggest_in_corner,k) = conj(mat.coeff(index_of_biggest_in_corner,k));
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mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k));
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}
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// partition the matrix:
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@@ -334,6 +325,16 @@ template<> struct ldlt_inplace<Lower>
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}
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if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
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A21 /= mat.coeffRef(k,k);
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if(sign)
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{
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// LDLT is not guaranteed to work for indefinite matrices, but let's try to get the sign right
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int newSign = numext::real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0;
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if(k == 0)
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*sign = newSign;
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else if(*sign != newSign)
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*sign = 0;
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}
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}
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return true;
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@@ -349,7 +350,7 @@ template<> struct ldlt_inplace<Lower>
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template<typename MatrixType, typename WDerived>
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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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using numext::isfinite;
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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typedef typename MatrixType::Index Index;
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@@ -367,9 +368,9 @@ template<> struct ldlt_inplace<Lower>
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break;
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// Update the diagonal terms
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RealScalar dj = real(mat.coeff(j,j));
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RealScalar dj = numext::real(mat.coeff(j,j));
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Scalar wj = w.coeff(j);
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RealScalar swj2 = sigma*abs2(wj);
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RealScalar swj2 = sigma*numext::abs2(wj);
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RealScalar gamma = dj*alpha + swj2;
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mat.coeffRef(j,j) += swj2/alpha;
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@@ -380,7 +381,7 @@ template<> struct ldlt_inplace<Lower>
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Index rs = size-j-1;
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w.tail(rs) -= wj * mat.col(j).tail(rs);
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if(gamma != 0)
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mat.col(j).tail(rs) += (sigma*conj(wj)/gamma)*w.tail(rs);
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mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
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}
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return true;
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}
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@@ -232,10 +232,10 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
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RealScalar beta = 1;
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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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RealScalar Ljj = numext::real(mat.coeff(j,j));
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RealScalar dj = numext::abs2(Ljj);
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Scalar wj = temp.coeff(j);
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RealScalar swj2 = sigma*abs2(wj);
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RealScalar swj2 = sigma*numext::abs2(wj);
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RealScalar gamma = dj*beta + swj2;
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RealScalar x = dj + swj2/beta;
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@@ -251,7 +251,7 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
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{
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temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
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if(gamma != 0)
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mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*conj(wj)/gamma)*temp.tail(rs);
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mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
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}
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}
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}
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@@ -277,7 +277,7 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
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Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
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Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
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RealScalar x = real(mat.coeff(k,k));
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RealScalar x = numext::real(mat.coeff(k,k));
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if (k>0) x -= A10.squaredNorm();
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if (x<=RealScalar(0))
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return k;
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