devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Replace local variables by member variables in compute() methods.

Browse Source 
This is to avoid dynamic memory allocations in the compute() methods of
ComplexEigenSolver, EigenSolver, and SelfAdjointEigenSolver where possible.
As a result, Tridiagonalization::decomposeInPlace() is no longer used.
Biggest remaining issue is the allocation in HouseholderSequence::evalTo().
This commit is contained in:
Jitse Niesen
2010-05-24 17:43:06 +01:00
parent 68820fd4e8
commit 7a43a4408b
4 changed files with 101 additions and 81 deletions
Download Patch File Download Diff File Expand all files Collapse all files

32
Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
View File

@@ -2,6 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -110,9 +111,10 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
* Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver.out
*/
SelfAdjointEigenSolver()
: m_eivec(int(Size), int(Size)),
m_eivalues(int(Size)),
m_subdiag(int(TridiagonalizationType::SizeMinusOne))
: m_eivec(),
m_eivalues(),
m_tridiag(),
m_subdiag()
{
ei_assert(Size!=Dynamic);
}
@@ -133,7 +135,8 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
SelfAdjointEigenSolver(int size)
: m_eivec(size, size),
m_eivalues(size),
m_subdiag(TridiagonalizationType::SizeMinusOne)
m_tridiag(size),
m_subdiag(size > 1 ? size - 1 : 1)
{}
/** \brief Constructor; computes eigendecomposition of given matrix.
@@ -157,9 +160,9 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
SelfAdjointEigenSolver(const MatrixType& matrix, bool computeEigenvectors = true)
: m_eivec(matrix.rows(), matrix.cols()),
m_eivalues(matrix.cols()),
m_subdiag()
m_tridiag(matrix.rows()),
m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1)
{
if (matrix.rows() > 1) m_subdiag.resize(matrix.rows() - 1);
compute(matrix, computeEigenvectors);
}
@@ -187,9 +190,9 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
SelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true)
: m_eivec(matA.rows(), matA.cols()),
m_eivalues(matA.cols()),
m_subdiag()
m_tridiag(matA.rows()),
m_subdiag(matA.rows() > 1 ? matA.rows() - 1 : 1)
{
if (matA.rows() > 1) m_subdiag.resize(matA.rows() - 1);
compute(matA, matB, computeEigenvectors);
}
@@ -351,6 +354,7 @@ template<typename _MatrixType> class SelfAdjointEigenSolver
protected:
MatrixType m_eivec;
RealVectorType m_eivalues;
TridiagonalizationType m_tridiag;
typename TridiagonalizationType::SubDiagonalType m_subdiag;
#ifndef NDEBUG
bool m_eigenvectorsOk;
@@ -396,14 +400,12 @@ SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>::compute(
return *this;
}
m_eivec = matrix;
// FIXME, should tridiag be a local variable of this function or an attribute of SelfAdjointEigenSolver ?
// the latter avoids multiple memory allocation when the same SelfAdjointEigenSolver is used multiple times...
// (same for diag and subdiag)
m_tridiag.compute(matrix);
RealVectorType& diag = m_eivalues;
m_subdiag.resize(n-1);
TridiagonalizationType::decomposeInPlace(m_eivec, diag, m_subdiag, computeEigenvectors);
diag = m_tridiag.diagonal();
m_subdiag = m_tridiag.subDiagonal();
if (computeEigenvectors)
m_eivec = m_tridiag.matrixQ();
int end = n-1;
int start = 0;