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37 #ifndef GMX_LINEARALGEBRA_EIGENSOLVER_H
38 #define GMX_LINEARALGEBRA_EIGENSOLVER_H
40 #include "../utility/real.h"
42 #include "sparsematrix.h"
48 /** Calculate eigenvalues/vectors a matrix stored in linear memory (not sparse).
50 * This routine uses lapack to diagonalize a matrix efficiently, and
51 * the eigenvalues/vectors will be sorted in ascending order on output.
52 * Gromacs comes with a built-in portable BLAS/LAPACK, but if performance
53 * matters it is advisable to link with an optimized vendor-provided library.
55 * \param a Pointer to matrix data, total size n*n
56 * The input data in the matrix will be destroyed/changed.
57 * \param n Side of the matrix to calculate eigenvalues for.
58 * \param index_lower Index of first eigenvector to determine.
59 * \param index_upper Last eigenvector determined is index_upper-1.
60 * \param eigenvalues Array of the eigenvalues on return. The length
61 * of this array _must_ be n, even if not all
62 * eigenvectors are calculated, since all eigenvalues
63 * might be needed as an intermediate step.
64 * \param eigenvec If this pointer is non-NULL, the eigenvectors
65 * specified by the indices are returned as rows of
66 * a matrix, i.e. eigenvector j starts at offset j*n, and
79 /*! \brief Sparse matrix eigensolver.
81 * This routine is intended for large matrices that might not fit in memory.
83 * It will determine the neig lowest eigenvalues, and if the eigenvectors pointer
84 * is non-NULL also the corresponding eigenvectors.
86 * maxiter=100000 should suffice in most cases!
89 sparse_eigensolver(gmx_sparsematrix_t * A,