src/tools/eigensolver.h

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  35
  36 #ifndef _EIGENSOLVER_H
  37 #define _EIGENSOLVER_H
  38
  39 #include "types/simple.h"
  40 #include "sparsematrix.h"
  41
  42
  43 /** Calculate eigenvalues/vectors a matrix stored in linear memory (not sparse).
  44  *
  45  *  This routine uses lapack to diagonalize a matrix efficiently, and
  46  *  the eigenvalues/vectors will be sorted in ascending order on output.
  47  *  Gromacs comes with a built-in portable BLAS/LAPACK, but if performance
  48  *  matters it is advisable to link with an optimized vendor-provided library.
  49  *
  50  *  \param a            Pointer to matrix data, total size n*n
  51  *                      The input data in the matrix will be destroyed/changed.
  52  *  \param n            Side of the matrix to calculate eigenvalues for.
  53  *  \param index_lower  Index of first eigenvector to determine.
  54  *  \param index_upper  Last eigenvector determined is index_upper-1.
  55  *  \param eigenvalues  Array of the eigenvalues on return. The length
  56  *                      of this array _must_ be n, even if not all
  57  *                      eigenvectors are calculated, since all eigenvalues
  58  *                      might be needed as an intermediate step.
  59  *  \param eigenvectors If this pointer is non-NULL, the eigenvectors
  60  *                      specified by the indices are returned as rows of
  61  *                      a matrix, i.e. eigenvector j starts at offset j*n, and
  62  *                      is of length n.
  63  */
  64 void
  65 eigensolver(real *   a,
  66             int      n,
  67             int      index_lower,
  68             int      index_upper,
  69             real *   eigenvalues,
  70             real *   eigenvec);
  71
  72
  73
  74 /*! \brief Sparse matrix eigensolver.
  75  *
  76  *  This routine is intended for large matrices that might not fit in memory.
  77  *
  78  *  It will determine the neig lowest eigenvalues, and if the eigenvectors pointer
  79  *  is non-NULL also the corresponding eigenvectors.
  80  *
  81  *  maxiter=100000 should suffice in most cases!
  82  */
  83 void
  84 sparse_eigensolver(gmx_sparsematrix_t *    A,
  85                    int                     neig,
  86                    real *                  eigenvalues,
  87                    real *                  eigenvectors,
  88                    int                     maxiter);
  89
  90
  91 #endif