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Generalize matrix ctor and compute() method of dense decomposition to 1) limit temporaries, 2) forward expressions to nested decompositions, 3) fix ambiguous ctor instanciation for square decomposition

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This commit is contained in:
Gael Guennebaud
2015-09-07 10:42:04 +02:00
parent 1702fcb72e
commit 7031a851d4
14 changed files with 162 additions and 90 deletions
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15
Eigen/src/Eigenvalues/ComplexEigenSolver.h
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@@ -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);
}

15
Eigen/src/Eigenvalues/ComplexSchur.h
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@@ -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;
}

15
Eigen/src/Eigenvalues/EigenSolver.h
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@@ -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();

10
Eigen/src/Eigenvalues/HessenbergDecomposition.h
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@@ -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;

13
Eigen/src/Eigenvalues/RealSchur.h
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@@ -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);

13
Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
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@@ -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

10
Eigen/src/Eigenvalues/Tridiagonalization.h
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@@ -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;