/usr/include/gromacs/linearalgebra/eigensolver.h is in libgromacs-dev 2016.1-2.
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#ifndef GMX_LINEARALGEBRA_EIGENSOLVER_H
#define GMX_LINEARALGEBRA_EIGENSOLVER_H
#include "gromacs/linearalgebra/sparsematrix.h"
#include "gromacs/utility/real.h"
#ifdef __cplusplus
extern "C" {
#endif
/** Calculate eigenvalues/vectors a matrix stored in linear memory (not sparse).
*
* This routine uses lapack to diagonalize a matrix efficiently, and
* the eigenvalues/vectors will be sorted in ascending order on output.
* Gromacs comes with a built-in portable BLAS/LAPACK, but if performance
* matters it is advisable to link with an optimized vendor-provided library.
*
* \param a Pointer to matrix data, total size n*n
* The input data in the matrix will be destroyed/changed.
* \param n Side of the matrix to calculate eigenvalues for.
* \param index_lower Index of first eigenvector to determine.
* \param index_upper Last eigenvector determined is index_upper-1.
* \param eigenvalues Array of the eigenvalues on return. The length
* of this array _must_ be n, even if not all
* eigenvectors are calculated, since all eigenvalues
* might be needed as an intermediate step.
* \param eigenvec If this pointer is non-NULL, the eigenvectors
* specified by the indices are returned as rows of
* a matrix, i.e. eigenvector j starts at offset j*n, and
* is of length n.
*/
void
eigensolver(real * a,
int n,
int index_lower,
int index_upper,
real * eigenvalues,
real * eigenvec);
/*! \brief Sparse matrix eigensolver.
*
* This routine is intended for large matrices that might not fit in memory.
*
* It will determine the neig lowest eigenvalues, and if the eigenvectors pointer
* is non-NULL also the corresponding eigenvectors.
*
* maxiter=100000 should suffice in most cases!
*/
void
sparse_eigensolver(gmx_sparsematrix_t * A,
int neig,
real * eigenvalues,
real * eigenvectors,
int maxiter);
#ifdef __cplusplus
}
#endif
#endif
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