mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-15 07:10:37 +08:00
e3fac69f19
This is the first step towards a non-selfadjoint eigen solver. Notes: - We might consider merging Tridiagonalization and Hessenberg toghether ? - Or we could factorize some code into a Householder class (could also be shared with QR)
65 lines
2.7 KiB
C++
65 lines
2.7 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra. Eigen itself is part of the KDE project.
|
|
//
|
|
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
|
|
//
|
|
// Eigen is free software; you can redistribute it and/or
|
|
// modify it under the terms of the GNU Lesser General Public
|
|
// License as published by the Free Software Foundation; either
|
|
// version 3 of the License, or (at your option) any later version.
|
|
//
|
|
// Alternatively, you can redistribute it and/or
|
|
// modify it under the terms of the GNU General Public License as
|
|
// published by the Free Software Foundation; either version 2 of
|
|
// the License, or (at your option) any later version.
|
|
//
|
|
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
|
|
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
|
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
|
|
// GNU General Public License for more details.
|
|
//
|
|
// You should have received a copy of the GNU Lesser General Public
|
|
// License and a copy of the GNU General Public License along with
|
|
// Eigen. If not, see <http://www.gnu.org/licenses/>.
|
|
|
|
#include "main.h"
|
|
#include <Eigen/QR>
|
|
|
|
template<typename MatrixType> void eigensolver(const MatrixType& m)
|
|
{
|
|
/* this test covers the following files:
|
|
EigenSolver.h, SelfAdjointEigenSolver.h (and indirectly: Tridiagonalization.h)
|
|
*/
|
|
int rows = m.rows();
|
|
int cols = m.cols();
|
|
|
|
typedef typename std::complex<typename NumTraits<typename MatrixType::Scalar>::Real> Complex;
|
|
|
|
MatrixType a = MatrixType::random(rows,cols);
|
|
MatrixType covMat = a.adjoint() * a;
|
|
|
|
SelfAdjointEigenSolver<MatrixType> eiSymm(covMat);
|
|
VERIFY_IS_APPROX(covMat * eiSymm.eigenvectors(), (eiSymm.eigenvectors() * eiSymm.eigenvalues().asDiagonal().eval()));
|
|
|
|
// EigenSolver<MatrixType> eiNotSymmButSymm(covMat);
|
|
// VERIFY_IS_APPROX((covMat.template cast<Complex>()) * (eiNotSymmButSymm.eigenvectors().template cast<Complex>()),
|
|
// (eiNotSymmButSymm.eigenvectors().template cast<Complex>()) * (eiNotSymmButSymm.eigenvalues().asDiagonal()));
|
|
|
|
// EigenSolver<MatrixType> eiNotSymm(a);
|
|
// VERIFY_IS_APPROX(a.template cast<Complex>() * eiNotSymm.eigenvectors().template cast<Complex>(),
|
|
// eiNotSymm.eigenvectors().template cast<Complex>() * eiNotSymm.eigenvalues().asDiagonal());
|
|
|
|
}
|
|
|
|
void test_eigensolver()
|
|
{
|
|
for(int i = 0; i < 1; i++) {
|
|
// very important to test a 3x3 matrix since we provide a special path for it
|
|
CALL_SUBTEST( eigensolver(Matrix3f()) );
|
|
CALL_SUBTEST( eigensolver(Matrix4d()) );
|
|
CALL_SUBTEST( eigensolver(MatrixXd(7,7)) );
|
|
CALL_SUBTEST( eigensolver(MatrixXcd(6,6)) );
|
|
CALL_SUBTEST( eigensolver(MatrixXcd(3,3)) );
|
|
}
|
|
}
|