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-rw-r--r--Eigen/src/Cholesky/LDLT.h13
-rw-r--r--Eigen/src/Cholesky/LLT.h13
-rw-r--r--Eigen/src/Eigenvalues/ComplexEigenSolver.h15
-rw-r--r--Eigen/src/Eigenvalues/ComplexSchur.h15
-rw-r--r--Eigen/src/Eigenvalues/EigenSolver.h15
-rw-r--r--Eigen/src/Eigenvalues/HessenbergDecomposition.h10
-rw-r--r--Eigen/src/Eigenvalues/RealSchur.h13
-rw-r--r--Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h13
-rw-r--r--Eigen/src/Eigenvalues/Tridiagonalization.h10
-rw-r--r--Eigen/src/LU/FullPivLU.h37
-rw-r--r--Eigen/src/LU/PartialPivLU.h16
-rw-r--r--Eigen/src/QR/ColPivHouseholderQR.h38
-rw-r--r--Eigen/src/QR/FullPivHouseholderQR.h31
-rw-r--r--Eigen/src/QR/HouseholderQR.h13
14 files changed, 162 insertions, 90 deletions
diff --git a/Eigen/src/Cholesky/LDLT.h b/Eigen/src/Cholesky/LDLT.h
index 602564399..6fcae01f7 100644
--- a/Eigen/src/Cholesky/LDLT.h
+++ b/Eigen/src/Cholesky/LDLT.h
@@ -99,14 +99,15 @@ template<typename _MatrixType, int _UpLo> class LDLT
* This calculates the decomposition for the input \a matrix.
* \sa LDLT(Index size)
*/
- explicit LDLT(const MatrixType& matrix)
+ template<typename InputType>
+ explicit LDLT(const EigenBase<InputType>& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_transpositions(matrix.rows()),
m_temporary(matrix.rows()),
m_sign(internal::ZeroSign),
m_isInitialized(false)
{
- compute(matrix);
+ compute(matrix.derived());
}
/** Clear any existing decomposition
@@ -188,7 +189,8 @@ template<typename _MatrixType, int _UpLo> class LDLT
template<typename Derived>
bool solveInPlace(MatrixBase<Derived> &bAndX) const;
- LDLT& compute(const MatrixType& matrix);
+ template<typename InputType>
+ LDLT& compute(const EigenBase<InputType>& matrix);
template <typename Derived>
LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
@@ -427,14 +429,15 @@ template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
*/
template<typename MatrixType, int _UpLo>
-LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a)
+template<typename InputType>
+LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
- m_matrix = a;
+ m_matrix = a.derived();
m_transpositions.resize(size);
m_isInitialized = false;
diff --git a/Eigen/src/Cholesky/LLT.h b/Eigen/src/Cholesky/LLT.h
index 745b74d95..dc73304e8 100644
--- a/Eigen/src/Cholesky/LLT.h
+++ b/Eigen/src/Cholesky/LLT.h
@@ -87,11 +87,12 @@ template<typename _MatrixType, int _UpLo> class LLT
explicit LLT(Index size) : m_matrix(size, size),
m_isInitialized(false) {}
- explicit LLT(const MatrixType& matrix)
+ template<typename InputType>
+ explicit LLT(const EigenBase<InputType>& matrix)
: m_matrix(matrix.rows(), matrix.cols()),
m_isInitialized(false)
{
- compute(matrix);
+ compute(matrix.derived());
}
/** \returns a view of the upper triangular matrix U */
@@ -131,7 +132,8 @@ template<typename _MatrixType, int _UpLo> class LLT
template<typename Derived>
void solveInPlace(MatrixBase<Derived> &bAndX) const;
- LLT& compute(const MatrixType& matrix);
+ template<typename InputType>
+ LLT& compute(const EigenBase<InputType>& matrix);
/** \returns the LLT decomposition matrix
*
@@ -381,14 +383,15 @@ template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
* Output: \verbinclude TutorialLinAlgComputeTwice.out
*/
template<typename MatrixType, int _UpLo>
-LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const MatrixType& a)
+template<typename InputType>
+LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const EigenBase<InputType>& a)
{
check_template_parameters();
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix.resize(size, size);
- m_matrix = a;
+ m_matrix = a.derived();
m_isInitialized = true;
bool ok = Traits::inplace_decomposition(m_matrix);
diff --git a/Eigen/src/Eigenvalues/ComplexEigenSolver.h b/Eigen/src/Eigenvalues/ComplexEigenSolver.h
index 6b010c312..ec3b1633e 100644
--- a/Eigen/src/Eigenvalues/ComplexEigenSolver.h
+++ b/Eigen/src/Eigenvalues/ComplexEigenSolver.h
@@ -122,7 +122,8 @@ template<typename _MatrixType> class ComplexEigenSolver
*
* This constructor calls compute() to compute the eigendecomposition.
*/
- explicit ComplexEigenSolver(const MatrixType& matrix, bool computeEigenvectors = true)
+ template<typename InputType>
+ explicit ComplexEigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
: m_eivec(matrix.rows(),matrix.cols()),
m_eivalues(matrix.cols()),
m_schur(matrix.rows()),
@@ -130,7 +131,7 @@ template<typename _MatrixType> class ComplexEigenSolver
m_eigenvectorsOk(false),
m_matX(matrix.rows(),matrix.cols())
{
- compute(matrix, computeEigenvectors);
+ compute(matrix.derived(), computeEigenvectors);
}
/** \brief Returns the eigenvectors of given matrix.
@@ -208,7 +209,8 @@ template<typename _MatrixType> class ComplexEigenSolver
* Example: \include ComplexEigenSolver_compute.cpp
* Output: \verbinclude ComplexEigenSolver_compute.out
*/
- ComplexEigenSolver& compute(const MatrixType& matrix, bool computeEigenvectors = true);
+ template<typename InputType>
+ ComplexEigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
/** \brief Reports whether previous computation was successful.
*
@@ -254,8 +256,9 @@ template<typename _MatrixType> class ComplexEigenSolver
template<typename MatrixType>
+template<typename InputType>
ComplexEigenSolver<MatrixType>&
-ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEigenvectors)
+ComplexEigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeEigenvectors)
{
check_template_parameters();
@@ -264,13 +267,13 @@ ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEi
// Do a complex Schur decomposition, A = U T U^*
// The eigenvalues are on the diagonal of T.
- m_schur.compute(matrix, computeEigenvectors);
+ m_schur.compute(matrix.derived(), computeEigenvectors);
if(m_schur.info() == Success)
{
m_eivalues = m_schur.matrixT().diagonal();
if(computeEigenvectors)
- doComputeEigenvectors(matrix.norm());
+ doComputeEigenvectors(m_schur.matrixT().norm());
sortEigenvalues(computeEigenvectors);
}
diff --git a/Eigen/src/Eigenvalues/ComplexSchur.h b/Eigen/src/Eigenvalues/ComplexSchur.h
index 993ee7e1e..7f38919f7 100644
--- a/Eigen/src/Eigenvalues/ComplexSchur.h
+++ b/Eigen/src/Eigenvalues/ComplexSchur.h
@@ -109,7 +109,8 @@ template<typename _MatrixType> class ComplexSchur
*
* \sa matrixT() and matrixU() for examples.
*/
- explicit ComplexSchur(const MatrixType& matrix, bool computeU = true)
+ template<typename InputType>
+ explicit ComplexSchur(const EigenBase<InputType>& matrix, bool computeU = true)
: m_matT(matrix.rows(),matrix.cols()),
m_matU(matrix.rows(),matrix.cols()),
m_hess(matrix.rows()),
@@ -117,7 +118,7 @@ template<typename _MatrixType> class ComplexSchur
m_matUisUptodate(false),
m_maxIters(-1)
{
- compute(matrix, computeU);
+ compute(matrix.derived(), computeU);
}
/** \brief Returns the unitary matrix in the Schur decomposition.
@@ -186,7 +187,8 @@ template<typename _MatrixType> class ComplexSchur
*
* \sa compute(const MatrixType&, bool, Index)
*/
- ComplexSchur& compute(const MatrixType& matrix, bool computeU = true);
+ template<typename InputType>
+ ComplexSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
/** \brief Compute Schur decomposition from a given Hessenberg matrix
* \param[in] matrixH Matrix in Hessenberg form H
@@ -313,14 +315,15 @@ typename ComplexSchur<MatrixType>::ComplexScalar ComplexSchur<MatrixType>::compu
template<typename MatrixType>
-ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::compute(const MatrixType& matrix, bool computeU)
+template<typename InputType>
+ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU)
{
m_matUisUptodate = false;
eigen_assert(matrix.cols() == matrix.rows());
if(matrix.cols() == 1)
{
- m_matT = matrix.template cast<ComplexScalar>();
+ m_matT = matrix.derived().template cast<ComplexScalar>();
if(computeU) m_matU = ComplexMatrixType::Identity(1,1);
m_info = Success;
m_isInitialized = true;
@@ -328,7 +331,7 @@ ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::compute(const MatrixType& ma
return *this;
}
- internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>::run(*this, matrix, computeU);
+ internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>::run(*this, matrix.derived(), computeU);
computeFromHessenberg(m_matT, m_matU, computeU);
return *this;
}
diff --git a/Eigen/src/Eigenvalues/EigenSolver.h b/Eigen/src/Eigenvalues/EigenSolver.h
index 8bc82df83..532ca7d63 100644
--- a/Eigen/src/Eigenvalues/EigenSolver.h
+++ b/Eigen/src/Eigenvalues/EigenSolver.h
@@ -110,7 +110,7 @@ template<typename _MatrixType> class EigenSolver
*
* \sa compute() for an example.
*/
- EigenSolver() : m_eivec(), m_eivalues(), m_isInitialized(false), m_realSchur(), m_matT(), m_tmp() {}
+ EigenSolver() : m_eivec(), m_eivalues(), m_isInitialized(false), m_realSchur(), m_matT(), m_tmp() {}
/** \brief Default constructor with memory preallocation
*
@@ -143,7 +143,8 @@ template<typename _MatrixType> class EigenSolver
*
* \sa compute()
*/
- explicit EigenSolver(const MatrixType& matrix, bool computeEigenvectors = true)
+ template<typename InputType>
+ explicit EigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
: m_eivec(matrix.rows(), matrix.cols()),
m_eivalues(matrix.cols()),
m_isInitialized(false),
@@ -152,7 +153,7 @@ template<typename _MatrixType> class EigenSolver
m_matT(matrix.rows(), matrix.cols()),
m_tmp(matrix.cols())
{
- compute(matrix, computeEigenvectors);
+ compute(matrix.derived(), computeEigenvectors);
}
/** \brief Returns the eigenvectors of given matrix.
@@ -273,7 +274,8 @@ template<typename _MatrixType> class EigenSolver
* Example: \include EigenSolver_compute.cpp
* Output: \verbinclude EigenSolver_compute.out
*/
- EigenSolver& compute(const MatrixType& matrix, bool computeEigenvectors = true);
+ template<typename InputType>
+ EigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
/** \returns NumericalIssue if the input contains INF or NaN values or overflow occured. Returns Success otherwise. */
ComputationInfo info() const
@@ -370,8 +372,9 @@ typename EigenSolver<MatrixType>::EigenvectorsType EigenSolver<MatrixType>::eige
}
template<typename MatrixType>
+template<typename InputType>
EigenSolver<MatrixType>&
-EigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEigenvectors)
+EigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeEigenvectors)
{
check_template_parameters();
@@ -381,7 +384,7 @@ EigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEigenvect
eigen_assert(matrix.cols() == matrix.rows());
// Reduce to real Schur form.
- m_realSchur.compute(matrix, computeEigenvectors);
+ m_realSchur.compute(matrix.derived(), computeEigenvectors);
m_info = m_realSchur.info();
diff --git a/Eigen/src/Eigenvalues/HessenbergDecomposition.h b/Eigen/src/Eigenvalues/HessenbergDecomposition.h
index 87a5bcb69..f647f69b0 100644
--- a/Eigen/src/Eigenvalues/HessenbergDecomposition.h
+++ b/Eigen/src/Eigenvalues/HessenbergDecomposition.h
@@ -115,8 +115,9 @@ template<typename _MatrixType> class HessenbergDecomposition
*
* \sa matrixH() for an example.
*/
- explicit HessenbergDecomposition(const MatrixType& matrix)
- : m_matrix(matrix),
+ template<typename InputType>
+ explicit HessenbergDecomposition(const EigenBase<InputType>& matrix)
+ : m_matrix(matrix.derived()),
m_temp(matrix.rows()),
m_isInitialized(false)
{
@@ -147,9 +148,10 @@ template<typename _MatrixType> class HessenbergDecomposition
* Example: \include HessenbergDecomposition_compute.cpp
* Output: \verbinclude HessenbergDecomposition_compute.out
*/
- HessenbergDecomposition& compute(const MatrixType& matrix)
+ template<typename InputType>
+ HessenbergDecomposition& compute(const EigenBase<InputType>& matrix)
{
- m_matrix = matrix;
+ m_matrix = matrix.derived();
if(matrix.rows()<2)
{
m_isInitialized = true;
diff --git a/Eigen/src/Eigenvalues/RealSchur.h b/Eigen/src/Eigenvalues/RealSchur.h
index 60ade50a0..f4ded69b6 100644
--- a/Eigen/src/Eigenvalues/RealSchur.h
+++ b/Eigen/src/Eigenvalues/RealSchur.h
@@ -100,7 +100,8 @@ template<typename _MatrixType> class RealSchur
* Example: \include RealSchur_RealSchur_MatrixType.cpp
* Output: \verbinclude RealSchur_RealSchur_MatrixType.out
*/
- explicit RealSchur(const MatrixType& matrix, bool computeU = true)
+ template<typename InputType>
+ explicit RealSchur(const EigenBase<InputType>& matrix, bool computeU = true)
: m_matT(matrix.rows(),matrix.cols()),
m_matU(matrix.rows(),matrix.cols()),
m_workspaceVector(matrix.rows()),
@@ -109,7 +110,7 @@ template<typename _MatrixType> class RealSchur
m_matUisUptodate(false),
m_maxIters(-1)
{
- compute(matrix, computeU);
+ compute(matrix.derived(), computeU);
}
/** \brief Returns the orthogonal matrix in the Schur decomposition.
@@ -165,7 +166,8 @@ template<typename _MatrixType> class RealSchur
*
* \sa compute(const MatrixType&, bool, Index)
*/
- RealSchur& compute(const MatrixType& matrix, bool computeU = true);
+ template<typename InputType>
+ RealSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
/** \brief Computes Schur decomposition of a Hessenberg matrix H = Z T Z^T
* \param[in] matrixH Matrix in Hessenberg form H
@@ -243,7 +245,8 @@ template<typename _MatrixType> class RealSchur
template<typename MatrixType>
-RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const MatrixType& matrix, bool computeU)
+template<typename InputType>
+RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU)
{
eigen_assert(matrix.cols() == matrix.rows());
Index maxIters = m_maxIters;
@@ -251,7 +254,7 @@ RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const MatrixType& matrix,
maxIters = m_maxIterationsPerRow * matrix.rows();
// Step 1. Reduce to Hessenberg form
- m_hess.compute(matrix);
+ m_hess.compute(matrix.derived());
// Step 2. Reduce to real Schur form
computeFromHessenberg(m_hess.matrixH(), m_hess.matrixQ(), computeU);
diff --git a/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
index c9331052a..39af73f98 100644
--- a/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
+++ b/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
@@ -158,13 +158,14 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
* \sa compute(const MatrixType&, int)
*/
EIGEN_DEVICE_FUNC
- explicit SelfAdjointEigenSolver(const MatrixType& matrix, int options = ComputeEigenvectors)
+ template<typename InputType>
+ explicit SelfAdjointEigenSolver(const EigenBase<InputType>& matrix, int options = ComputeEigenvectors)
: m_eivec(matrix.rows(), matrix.cols()),
m_eivalues(matrix.cols()),
m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1),
m_isInitialized(false)
{
- compute(matrix, options);
+ compute(matrix.derived(), options);
}
/** \brief Computes eigendecomposition of given matrix.
@@ -198,7 +199,8 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
* \sa SelfAdjointEigenSolver(const MatrixType&, int)
*/
EIGEN_DEVICE_FUNC
- SelfAdjointEigenSolver& compute(const MatrixType& matrix, int options = ComputeEigenvectors);
+ template<typename InputType>
+ SelfAdjointEigenSolver& compute(const EigenBase<InputType>& matrix, int options = ComputeEigenvectors);
/** \brief Computes eigendecomposition of given matrix using a closed-form algorithm
*
@@ -389,12 +391,15 @@ static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index sta
}
template<typename MatrixType>
+template<typename InputType>
EIGEN_DEVICE_FUNC
SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
-::compute(const MatrixType& matrix, int options)
+::compute(const EigenBase<InputType>& a_matrix, int options)
{
check_template_parameters();
+ const InputType &matrix(a_matrix);
+
using std::abs;
eigen_assert(matrix.cols() == matrix.rows());
eigen_assert((options&~(EigVecMask|GenEigMask))==0
diff --git a/Eigen/src/Eigenvalues/Tridiagonalization.h b/Eigen/src/Eigenvalues/Tridiagonalization.h
index f21331312..2030b5be1 100644
--- a/Eigen/src/Eigenvalues/Tridiagonalization.h
+++ b/Eigen/src/Eigenvalues/Tridiagonalization.h
@@ -126,8 +126,9 @@ template<typename _MatrixType> class Tridiagonalization
* Example: \include Tridiagonalization_Tridiagonalization_MatrixType.cpp
* Output: \verbinclude Tridiagonalization_Tridiagonalization_MatrixType.out
*/
- explicit Tridiagonalization(const MatrixType& matrix)
- : m_matrix(matrix),
+ template<typename InputType>
+ explicit Tridiagonalization(const EigenBase<InputType>& matrix)
+ : m_matrix(matrix.derived()),
m_hCoeffs(matrix.cols() > 1 ? matrix.cols()-1 : 1),
m_isInitialized(false)
{
@@ -152,9 +153,10 @@ template<typename _MatrixType> class Tridiagonalization
* Example: \include Tridiagonalization_compute.cpp
* Output: \verbinclude Tridiagonalization_compute.out
*/
- Tridiagonalization& compute(const MatrixType& matrix)
+ template<typename InputType>
+ Tridiagonalization& compute(const EigenBase<InputType>& matrix)
{
- m_matrix = matrix;
+ m_matrix = matrix.derived();
m_hCoeffs.resize(matrix.rows()-1, 1);
internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
m_isInitialized = true;
diff --git a/Eigen/src/LU/FullPivLU.h b/Eigen/src/LU/FullPivLU.h
index 278bc1591..07a87cbc6 100644
--- a/Eigen/src/LU/FullPivLU.h
+++ b/Eigen/src/LU/FullPivLU.h
@@ -95,7 +95,8 @@ template<typename _MatrixType> class FullPivLU
* \param matrix the matrix of which to compute the LU decomposition.
* It is required to be nonzero.
*/
- explicit FullPivLU(const MatrixType& matrix);
+ template<typename InputType>
+ explicit FullPivLU(const EigenBase<InputType>& matrix);
/** Computes the LU decomposition of the given matrix.
*
@@ -104,7 +105,8 @@ template<typename _MatrixType> class FullPivLU
*
* \returns a reference to *this
*/
- FullPivLU& compute(const MatrixType& matrix);
+ template<typename InputType>
+ FullPivLU& compute(const EigenBase<InputType>& matrix);
/** \returns the LU decomposition matrix: the upper-triangular part is U, the
* unit-lower-triangular part is L (at least for square matrices; in the non-square
@@ -396,6 +398,8 @@ template<typename _MatrixType> class FullPivLU
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
+ void computeInPlace();
+
MatrixType m_lu;
PermutationPType m_p;
PermutationQType m_q;
@@ -425,7 +429,8 @@ FullPivLU<MatrixType>::FullPivLU(Index rows, Index cols)
}
template<typename MatrixType>
-FullPivLU<MatrixType>::FullPivLU(const MatrixType& matrix)
+template<typename InputType>
+FullPivLU<MatrixType>::FullPivLU(const EigenBase<InputType>& matrix)
: m_lu(matrix.rows(), matrix.cols()),
m_p(matrix.rows()),
m_q(matrix.cols()),
@@ -434,11 +439,12 @@ FullPivLU<MatrixType>::FullPivLU(const MatrixType& matrix)
m_isInitialized(false),
m_usePrescribedThreshold(false)
{
- compute(matrix);
+ compute(matrix.derived());
}
template<typename MatrixType>
-FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix)
+template<typename InputType>
+FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const EigenBase<InputType>& matrix)
{
check_template_parameters();
@@ -446,16 +452,24 @@ FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix)
eigen_assert(matrix.rows()<=NumTraits<int>::highest() && matrix.cols()<=NumTraits<int>::highest());
m_isInitialized = true;
- m_lu = matrix;
+ m_lu = matrix.derived();
+
+ computeInPlace();
+
+ return *this;
+}
- const Index size = matrix.diagonalSize();
- const Index rows = matrix.rows();
- const Index cols = matrix.cols();
+template<typename MatrixType>
+void FullPivLU<MatrixType>::computeInPlace()
+{
+ const Index size = m_lu.diagonalSize();
+ const Index rows = m_lu.rows();
+ const Index cols = m_lu.cols();
// will store the transpositions, before we accumulate them at the end.
// can't accumulate on-the-fly because that will be done in reverse order for the rows.
- m_rowsTranspositions.resize(matrix.rows());
- m_colsTranspositions.resize(matrix.cols());
+ m_rowsTranspositions.resize(m_lu.rows());
+ m_colsTranspositions.resize(m_lu.cols());
Index number_of_transpositions = 0; // number of NONTRIVIAL transpositions, i.e. m_rowsTranspositions[i]!=i
m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
@@ -527,7 +541,6 @@ FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix)
m_q.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k));
m_det_pq = (number_of_transpositions%2) ? -1 : 1;
- return *this;
}
template<typename MatrixType>
diff --git a/Eigen/src/LU/PartialPivLU.h b/Eigen/src/LU/PartialPivLU.h
index 019fc4230..2c28818a3 100644
--- a/Eigen/src/LU/PartialPivLU.h
+++ b/Eigen/src/LU/PartialPivLU.h
@@ -102,9 +102,11 @@ template<typename _MatrixType> class PartialPivLU
* \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
* If you need to deal with non-full rank, use class FullPivLU instead.
*/
- explicit PartialPivLU(const MatrixType& matrix);
+ template<typename InputType>
+ explicit PartialPivLU(const EigenBase<InputType>& matrix);
- PartialPivLU& compute(const MatrixType& matrix);
+ template<typename InputType>
+ PartialPivLU& compute(const EigenBase<InputType>& matrix);
/** \returns the LU decomposition matrix: the upper-triangular part is U, the
* unit-lower-triangular part is L (at least for square matrices; in the non-square
@@ -243,14 +245,15 @@ PartialPivLU<MatrixType>::PartialPivLU(Index size)
}
template<typename MatrixType>
-PartialPivLU<MatrixType>::PartialPivLU(const MatrixType& matrix)
+template<typename InputType>
+PartialPivLU<MatrixType>::PartialPivLU(const EigenBase<InputType>& matrix)
: m_lu(matrix.rows(), matrix.rows()),
m_p(matrix.rows()),
m_rowsTranspositions(matrix.rows()),
m_det_p(0),
m_isInitialized(false)
{
- compute(matrix);
+ compute(matrix.derived());
}
namespace internal {
@@ -429,14 +432,15 @@ void partial_lu_inplace(MatrixType& lu, TranspositionType& row_transpositions, t
} // end namespace internal
template<typename MatrixType>
-PartialPivLU<MatrixType>& PartialPivLU<MatrixType>::compute(const MatrixType& matrix)
+template<typename InputType>
+PartialPivLU<MatrixType>& PartialPivLU<MatrixType>::compute(const EigenBase<InputType>& matrix)
{
check_template_parameters();
// the row permutation is stored as int indices, so just to be sure:
eigen_assert(matrix.rows()<NumTraits<int>::highest());
- m_lu = matrix;
+ m_lu = matrix.derived();
eigen_assert(matrix.rows() == matrix.cols() && "PartialPivLU is only for square (and moreover invertible) matrices");
const Index size = matrix.rows();
diff --git a/Eigen/src/QR/ColPivHouseholderQR.h b/Eigen/src/QR/ColPivHouseholderQR.h
index 7b3842cbe..172e4a89f 100644
--- a/Eigen/src/QR/ColPivHouseholderQR.h
+++ b/Eigen/src/QR/ColPivHouseholderQR.h
@@ -118,7 +118,8 @@ template<typename _MatrixType> class ColPivHouseholderQR
*
* \sa compute()
*/
- explicit ColPivHouseholderQR(const MatrixType& matrix)
+ template<typename InputType>
+ explicit ColPivHouseholderQR(const EigenBase<InputType>& matrix)
: m_qr(matrix.rows(), matrix.cols()),
m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
m_colsPermutation(PermIndexType(matrix.cols())),
@@ -128,7 +129,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
m_isInitialized(false),
m_usePrescribedThreshold(false)
{
- compute(matrix);
+ compute(matrix.derived());
}
/** This method finds a solution x to the equation Ax=b, where A is the matrix of which
@@ -185,7 +186,8 @@ template<typename _MatrixType> class ColPivHouseholderQR
return m_qr;
}
- ColPivHouseholderQR& compute(const MatrixType& matrix);
+ template<typename InputType>
+ ColPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
/** \returns a const reference to the column permutation matrix */
const PermutationType& colsPermutation() const
@@ -404,6 +406,8 @@ template<typename _MatrixType> class ColPivHouseholderQR
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
+ void computeInPlace();
+
MatrixType m_qr;
HCoeffsType m_hCoeffs;
PermutationType m_colsPermutation;
@@ -440,24 +444,34 @@ typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::logAbsDetermina
* \sa class ColPivHouseholderQR, ColPivHouseholderQR(const MatrixType&)
*/
template<typename MatrixType>
-ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix)
+template<typename InputType>
+ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
{
check_template_parameters();
- using std::abs;
- Index rows = matrix.rows();
- Index cols = matrix.cols();
- Index size = matrix.diagonalSize();
-
// the column permutation is stored as int indices, so just to be sure:
- eigen_assert(cols<=NumTraits<int>::highest());
+ eigen_assert(matrix.cols()<=NumTraits<int>::highest());
m_qr = matrix;
+
+ computeInPlace();
+
+ return *this;
+}
+
+template<typename MatrixType>
+void ColPivHouseholderQR<MatrixType>::computeInPlace()
+{
+ using std::abs;
+ Index rows = m_qr.rows();
+ Index cols = m_qr.cols();
+ Index size = m_qr.diagonalSize();
+
m_hCoeffs.resize(size);
m_temp.resize(cols);
- m_colsTranspositions.resize(matrix.cols());
+ m_colsTranspositions.resize(m_qr.cols());
Index number_of_transpositions = 0;
m_colSqNorms.resize(cols);
@@ -522,8 +536,6 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const
m_det_pq = (number_of_transpositions%2) ? -1 : 1;
m_isInitialized = true;
-
- return *this;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
diff --git a/Eigen/src/QR/FullPivHouseholderQR.h b/Eigen/src/QR/FullPivHouseholderQR.h
index 4c2c958a8..64fe6b7b8 100644
--- a/Eigen/src/QR/FullPivHouseholderQR.h
+++ b/Eigen/src/QR/FullPivHouseholderQR.h
@@ -121,7 +121,8 @@ template<typename _MatrixType> class FullPivHouseholderQR
*
* \sa compute()
*/
- explicit FullPivHouseholderQR(const MatrixType& matrix)
+ template<typename InputType>
+ explicit FullPivHouseholderQR(const EigenBase<InputType>& matrix)
: m_qr(matrix.rows(), matrix.cols()),
m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
@@ -131,7 +132,7 @@ template<typename _MatrixType> class FullPivHouseholderQR
m_isInitialized(false),
m_usePrescribedThreshold(false)
{
- compute(matrix);
+ compute(matrix.derived());
}
/** This method finds a solution x to the equation Ax=b, where A is the matrix of which
@@ -172,7 +173,8 @@ template<typename _MatrixType> class FullPivHouseholderQR
return m_qr;
}
- FullPivHouseholderQR& compute(const MatrixType& matrix);
+ template<typename InputType>
+ FullPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
/** \returns a const reference to the column permutation matrix */
const PermutationType& colsPermutation() const
@@ -386,6 +388,8 @@ template<typename _MatrixType> class FullPivHouseholderQR
EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
}
+ void computeInPlace();
+
MatrixType m_qr;
HCoeffsType m_hCoeffs;
IntDiagSizeVectorType m_rows_transpositions;
@@ -423,16 +427,27 @@ typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::logAbsDetermin
* \sa class FullPivHouseholderQR, FullPivHouseholderQR(const MatrixType&)
*/
template<typename MatrixType>
-FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix)
+template<typename InputType>
+FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
{
check_template_parameters();
+ m_qr = matrix.derived();
+
+ computeInPlace();
+
+ return *this;
+}
+
+template<typename MatrixType>
+void FullPivHouseholderQR<MatrixType>::computeInPlace()
+{
using std::abs;
- Index rows = matrix.rows();
- Index cols = matrix.cols();
+ Index rows = m_qr.rows();
+ Index cols = m_qr.cols();
Index size = (std::min)(rows,cols);
- m_qr = matrix;
+
m_hCoeffs.resize(size);
m_temp.resize(cols);
@@ -503,8 +518,6 @@ FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(cons
m_det_pq = (number_of_transpositions%2) ? -1 : 1;
m_isInitialized = true;
-
- return *this;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
diff --git a/Eigen/src/QR/HouseholderQR.h b/Eigen/src/QR/HouseholderQR.h
index 878654be5..1eb861025 100644
--- a/Eigen/src/QR/HouseholderQR.h
+++ b/Eigen/src/QR/HouseholderQR.h
@@ -92,13 +92,14 @@ template<typename _MatrixType> class HouseholderQR
*
* \sa compute()
*/
- explicit HouseholderQR(const MatrixType& matrix)
+ template<typename InputType>
+ explicit HouseholderQR(const EigenBase<InputType>& matrix)
: m_qr(matrix.rows(), matrix.cols()),
m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
m_temp(matrix.cols()),
m_isInitialized(false)
{
- compute(matrix);
+ compute(matrix.derived());
}
/** This method finds a solution x to the equation Ax=b, where A is the matrix of which
@@ -149,7 +150,8 @@ template<typename _MatrixType> class HouseholderQR
return m_qr;
}
- HouseholderQR& compute(const MatrixType& matrix);
+ template<typename InputType>
+ HouseholderQR& compute(const EigenBase<InputType>& matrix);
/** \returns the absolute value of the determinant of the matrix of which
* *this is the QR decomposition. It has only linear complexity
@@ -352,7 +354,8 @@ void HouseholderQR<_MatrixType>::_solve_impl(const RhsType &rhs, DstType &dst) c
* \sa class HouseholderQR, HouseholderQR(const MatrixType&)
*/
template<typename MatrixType>
-HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType& matrix)
+template<typename InputType>
+HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const EigenBase<InputType>& matrix)
{
check_template_parameters();
@@ -360,7 +363,7 @@ HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType&
Index cols = matrix.cols();
Index size = (std::min)(rows,cols);
- m_qr = matrix;
+ m_qr = matrix.derived();
m_hCoeffs.resize(size);
m_temp.resize(cols);
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-08 10:23:12 +0000