src/gromacs/linearalgebra/eigensolver.h

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  37 #ifndef GMX_LINEARALGEBRA_EIGENSOLVER_H
  38 #define GMX_LINEARALGEBRA_EIGENSOLVER_H
  39
  40 #include "gromacs/linearalgebra/sparsematrix.h"
  41 #include "gromacs/utility/real.h"
  42
  43 #ifdef __cplusplus
  44 extern "C" {
  45 #endif
  46
  47 /** Calculate eigenvalues/vectors a matrix stored in linear memory (not sparse).
  48  *
  49  *  This routine uses lapack to diagonalize a matrix efficiently, and
  50  *  the eigenvalues/vectors will be sorted in ascending order on output.
  51  *  Gromacs comes with a built-in portable BLAS/LAPACK, but if performance
  52  *  matters it is advisable to link with an optimized vendor-provided library.
  53  *
  54  *  \param a            Pointer to matrix data, total size n*n
  55  *                      The input data in the matrix will be destroyed/changed.
  56  *  \param n            Side of the matrix to calculate eigenvalues for.
  57  *  \param index_lower  Index of first eigenvector to determine.
  58  *  \param index_upper  Last eigenvector determined is index_upper-1.
  59  *  \param eigenvalues  Array of the eigenvalues on return. The length
  60  *                      of this array _must_ be n, even if not all
  61  *                      eigenvectors are calculated, since all eigenvalues
  62  *                      might be needed as an intermediate step.
  63  *  \param eigenvec     If this pointer is non-NULL, the eigenvectors
  64  *                      specified by the indices are returned as rows of
  65  *                      a matrix, i.e. eigenvector j starts at offset j*n, and
  66  *                      is of length n.
  67  */
  68 void
  69 eigensolver(real *   a,
  70             int      n,
  71             int      index_lower,
  72             int      index_upper,
  73             real *   eigenvalues,
  74             real *   eigenvec);
  75
  76
  77
  78 /*! \brief Sparse matrix eigensolver.
  79  *
  80  *  This routine is intended for large matrices that might not fit in memory.
  81  *
  82  *  It will determine the neig lowest eigenvalues, and if the eigenvectors pointer
  83  *  is non-NULL also the corresponding eigenvectors.
  84  *
  85  *  maxiter=100000 should suffice in most cases!
  86  */
  87 void
  88 sparse_eigensolver(gmx_sparsematrix_t *    A,
  89                    int                     neig,
  90                    real *                  eigenvalues,
  91                    real *                  eigenvectors,
  92                    int                     maxiter);
  93
  94 #ifdef __cplusplus
  95 }
  96 #endif
  97
  98 #endif