syev.hpp

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//===========================================================================
/*!
 * 
 *
 * \brief      Contains the lapack bindings for the symmetric eigenvalue problem syev.
 *
 * \author      O. Krause
 * \date        2010
 *
 *
 * \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 REMORA_KERNELS_LAPACK_SYEV_HPP
#define REMORA_KERNELS_LAPACK_SYEV_HPP
 
#include "fortran.hpp"
#include "../../detail/traits.hpp"
 
#define REMORA_LAPACK_DSYEV FORTRAN_ID(dsyev)
 
extern "C"{
void REMORA_LAPACK_DSYEV( 
    const char* jobz, const char* uplo, const int *n,
    double* a, const int * lda, double* w,
    double* work, const int * lwork, int* info
);
}
 
 
 
namespace remora {namespace bindings {
 
inline void syev(
    int n, bool upper,
    double* A, int lda,
    double* eigenvalues
){
    if(n == 0) return;
    int lwork = std::min<int>(130,4*n)*n;
    double* work = new double[lwork];
    int info;
    char job = 'V';
    char uplo = upper?'U':'L';
    REMORA_LAPACK_DSYEV(&job, &uplo, &n, A, &lda,eigenvalues,work,&lwork,&info);
    delete[] work;
    
}
 
 
template <typename MatA, typename VectorB>
void syev(
    matrix_expression<MatA, cpu_tag>& A,
    vector_expression<VectorB, cpu_tag>& eigenValues
) {
    REMORA_SIZE_CHECK(A().size1() == A().size2());
    REMORA_SIZE_CHECK(A().size1() == eigenValues().size());
    
    std::size_t n = A().size1();
    bool upper = false;
    //lapack is column major storage.
    if(std::is_same<typename MatA::orientation, row_major>::value){
        upper = !upper;
    }
    auto storageA = A().raw_storage();
    auto storageEig = eigenValues().raw_storage();
    syev(
        n, upper,
        storageA.values,
            storageA.leading_dimension,
        storageEig.values
    );
    
    A() = trans(A);
    
    //reverse eigenvectors and eigenvalues
    for (int i = 0; i < (int)n-i-1; i++)
    {
        int l =  n-i-1;
        std::swap(eigenValues()( l ),eigenValues()( i ));
    }
    for (int j = 0; j < (int)n; j++) {
        for (int i = 0; i < (int)n-i-1; i++)
        {
            int l =  n-i-1;
            std::swap(A()( j , l ), A()( j , i ));
        }
    }
}
 
}}
 
#undef REMORA_LAPACK_DSYEV
 
#endif