/usr/include/shark/LinAlg/eigenvalues.h is in libshark-dev 3.1.3+ds1-2.
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*
*
* \brief Algorithms for Eigenvalue decompositions
*
*
*
*
* \author O. Krause
* \date 2011
*
*
* \par Copyright 1995-2015 Shark Development Team
*
* <BR><HR>
* This file is part of Shark.
* <http://image.diku.dk/shark/>
*
* Shark 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.
*
* Shark 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 for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Shark. If not, see <http://www.gnu.org/licenses/>.
*
*/
#ifndef SHARK_LINALG_EIGENVALUES_H
#define SHARK_LINALG_EIGENVALUES_H
#include <shark/LinAlg/Base.h>
#include <shark/LinAlg/BLAS/kernels/syev.hpp>
namespace shark{ namespace blas{
/**
* \ingroup shark_globals
*
* @{
*/
/*!
* \brief Used as frontend for
* eigensymm for calculating the eigenvalues and the normalized eigenvectors of a symmetric matrix
* 'A' using the Givens and Householder reduction. Each time this frontend is called additional
* memory is allocated for intermediate results.
*
*
* \param A \f$ n \times n \f$ matrix, which must be symmetric, so only the bottom triangular matrix must contain values.
* \param eigenVectors \f$ n \times n \f$ matrix with the calculated normalized eigenvectors, each column contains an eigenvector.
* \param eigenValues n-dimensional vector with the calculated eigenvalues in descending order.
* \return none.
*
* \throw SharkException
*/
template<class MatrixT,class MatrixU,class VectorT>
void eigensymm
(
matrix_expression<MatrixT> const& A,
matrix_expression<MatrixU>& eigenVectors,
vector_expression<VectorT>& eigenValues
)
{
SIZE_CHECK(A().size2() == A().size1());
std::size_t n = A().size1();
eigenVectors().resize(n,n);
eigenVectors().clear();
eigenValues().resize(n);
eigenValues().clear();
// special case n = 1
if (n == 1) {
eigenVectors()( 0 , 0 ) = 1;
eigenValues()( 0 ) = A()( 0 , 0 );
return;
}
// copy matrix
for (std::size_t i = 0; i < n; i++) {
for (std::size_t j = 0; j <= i; j++) {
eigenVectors()(i, j) = A()(i, j);
}
}
kernels::syev(eigenVectors,eigenValues);
}
/** @}*/
}}
#endif
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