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  /external/eigen/Eigen/src/Householder/
BlockHouseholder.h 22 void make_block_householder_triangular_factor(TriangularFactorType& triFactor, const VectorsType& vectors, const CoeffsType& hCoeffs)
34 triFactor.col(i).head(i).noalias() = -hCoeffs(i) * vectors.block(i, 0, rs, i).adjoint()
40 triFactor(i,i) = hCoeffs(i);
46 void apply_block_householder_on_the_left(MatrixType& mat, const VectorsType& vectors, const CoeffsType& hCoeffs)
52 make_block_householder_triangular_factor(T, vectors, hCoeffs);
  /external/eigen/Eigen/src/QR/
HouseholderQR_MKL.h 46 template<typename MatrixQR, typename HCoeffs> \
47 void householder_qr_inplace_blocked(MatrixQR& mat, HCoeffs& hCoeffs, \
55 LAPACKE_##MKLPREFIX##geqrf( matrix_order, m, n, (MKLTYPE*)mat.data(), lda, (MKLTYPE*)hCoeffs.data()); \
56 hCoeffs.adjointInPlace(); \
HouseholderQR.h 164 const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
192 template<typename MatrixQR, typename HCoeffs>
193 void householder_qr_inplace_unblocked(MatrixQR& mat, HCoeffs& hCoeffs, typename MatrixQR::Scalar* tempData = 0)
202 eigen_assert(hCoeffs.size() == size);
218 mat.col(k).tail(remainingRows).makeHouseholderInPlace(hCoeffs.coeffRef(k), beta);
223 .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows-1), hCoeffs.coeffRef(k), tempData+k+1);
228 template<typename MatrixQR, typename HCoeffs>
229 void householder_qr_inplace_blocked(MatrixQR& mat, HCoeffs& hCoeffs,
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FullPivHouseholderQR.h 277 const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
488 dec().hCoeffs().coeff(k), &temp.coeffRef(0));
529 const HCoeffsType& hCoeffs,
532 m_hCoeffs(hCoeffs),
ColPivHouseholderQR.h 258 const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
473 c.applyOnTheLeft(householderSequence(dec().matrixQR(), dec().hCoeffs())
  /external/eigen/Eigen/src/Eigenvalues/
Tridiagonalization.h 26 void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs);
328 * \param[out] hCoeffs returned Householder coefficients (see below)
337 * where \f$ h_i = hCoeffs[i]\f$ is the \f$ i \f$th Householder coefficient and
346 void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs)
353 eigen_assert(n==hCoeffs.size()+1 || n==1);
366 hCoeffs.tail(n-i-1).noalias() = (matA.bottomRightCorner(remainingSize,remainingSize).template selfadjointView<Lower>()
369 hCoeffs.tail(n-i-1) += (conj(h)*Scalar(-0.5)*(hCoeffs.tail(remainingSize).dot(matA.col(i).tail(remainingSize)))) * matA.col(i).tail(n-i-1);
372 .rankUpdate(matA.col(i).tail(remainingSize), hCoeffs.tail(remainingSize), -1);
375 hCoeffs.coeffRef(i) = h
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HessenbergDecomposition.h 270 static void _compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp);
283 * \param hCoeffs returned Householder coefficients
292 void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp)
305 hCoeffs.coeffRef(i) = h;
SelfAdjointEigenSolver.h 370 * \param hCoeffs returned Householder coefficients
  /external/eigen/test/
householder.cpp 89 HCoeffsVectorType hc = qr.hCoeffs().conjugate();
  /external/eigen/unsupported/Eigen/src/NonLinearOptimization/
LevenbergMarquardt.h 461 fjac.diagonal() = qrfac.hCoeffs();

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