Create a new sparse matrix in getfem++ format. These sparse matrix can be stored as CSC (compressed column
sparse), which is the format used by Matlab, or they can be stored as WSC
(internal format to getfem). The CSC matrices are not writable (it would
be very inefficient), but they are optimized for multiplication with
vectors, and memory usage. The WSC are writable, they are very fast with
respect to random read/write operation. However their memory overhead is
higher than CSC matrices, and they are a little bit slower for
matrix-vector multiplications.
By default, all newly created matrices are build as WSC matrices. This can
be changed later with gf_spmat_set(spmat S, 'to_csc',...)
, or may be changed
automatically by getfem (for example
gf_linsolve()
converts the
matrices to CSC).
The matrices may store REAL or COMPLEX values.
SM = gf_spmat('empty', int m [, int n])
SM = gf_spmat('copy', mat K [, I [, J]])
SM = gf_spmat('identity', int n)
SM = gf_spmat('mult', spmat A, spmat B)
SM = gf_spmat('add', spmat A, spmat B)
SM = gf_spmat('diag', mat D [, ivec E [, int n [,int m]]])
SM = gf_spmat('load','hb'|'harwell-boeing'|'mm'|'matrix-market', string filename)
General constructor for spmat objects.
Create a new sparse matrix in getfem++ format. These sparse matrix can be stored as CSC (compressed column
sparse), which is the format used by Matlab, or they can be stored as WSC
(internal format to getfem). The CSC matrices are not writable (it would
be very inefficient), but they are optimized for multiplication with
vectors, and memory usage. The WSC are writable, they are very fast with
respect to random read/write operation. However their memory overhead is
higher than CSC matrices, and they are a little bit slower for
matrix-vector multiplications.
By default, all newly created matrices are build as WSC matrices. This can
be changed later with gf_spmat_set(spmat S, 'to_csc',...)
, or may be changed
automatically by getfem (for example
gf_linsolve()
converts the
matrices to CSC).
The matrices may store REAL or COMPLEX values.
SM = gf_spmat('empty', int m [, int n])
Create a new empty (i.e. full of zeros) sparse matrix, of dimensions
m x n
. If n
is omitted, the matrix dimension is m x m
.
SM = gf_spmat('copy', mat K [, I [, J]])
Duplicate a matrix K
(which might be a spmat). If index I
and/or J
are given, the matrix will
be a submatrix of K
. For example::
m = gf_spmat('copy', sprand(50,50,.1), 1:40, [6 7 8 3 10])
will return a 40x5 matrix.
SM = gf_spmat('identity', int n)
Create a n x n
identity matrix.
SM = gf_spmat('mult', spmat A, spmat B)
Create a sparse matrix as the product of the sparse matrices A
and
B
. It requires that A
and B
be both real or both complex, you
may have to use gf_spmat_set(spmat S, 'to_complex')
SM = gf_spmat('add', spmat A, spmat B)
Create a sparse matrix as the sum of the sparse matrices A
and B
.
Adding a real matrix with a complex matrix is possible.
SM = gf_spmat('diag', mat D [, ivec E [, int n [,int m]]])
Create a diagonal matrix. If E
is given, D
might be a matrix and
each column of E
will contain the sub-diagonal number that will be
filled with the corresponding column of D
.
SM = gf_spmat('load','hb'|'harwell-boeing'|'mm'|'matrix-market', string filename)
Read a sparse matrix from an Harwell-Boeing or a Matrix-Market file .
Y. Collette