gf_spmat_get — General getfem sparse matrix inquiry function.
ivec gf_spmat_get(M,'size') int gf_spmat_get(M,'nnz') int gf_spmat_get(M,'is_complex') string S=gf_spmat_get(M,'storage') mat fM=gf_spmat_get(M,'full'[,I [,J]]) vec tMV=gf_spmat_get(M,'mult', V) vec MV=gf_spmat_get(M,'tmult', V) vec D=gf_spmat_get(M,'diag'[, E]) [ivec JC,ivec IR]=gf_spmat_get(M,'csc_ind') [vec V]=gf_spmat_get(M,'csc_val') [spmat N, vec U0]=gf_spmat_get(H,'dirichlet_nullspace', @vec R) string S=gf_spmat_get(sl,'info')gf_spmat_get(sl,'save', string format, string filename)
General getfem sparse matrix inquiry function. M might also be a native matlab sparse matrix.
n = gf_spmat_get(M,'nnz') Return the number of non-null values stored in the sparse matrix
Sm = gf_spmat_get(M,'full'[, list I[, list J]]) Return a full
(sub-)matrix. The optional arguments I
and
J
, are the sub-intervals for the rows and columns
that are to be extracted.
MV = gf_spmat_get(M,'mult',vec V) Product of the sparse matrix
M
with a vector V
. For
matrix-matrix multiplications, see gf_spmat('mult').
MtV = gf_spmat_get(M,'tmult',vec V) Product of
M
transposed (conjugated if M is complex) with the
vector V.
D = gf_spmat_get(M,'diag'[, list E]) Return the diagonal of
M
as a vector. If E
is used,
return the sub-diagonals whose ranks are given in
E
.
s = gf_spmat_get(M,'storage') Return the storage type currently used for the matrix. The storage is returned as a string, either 'CSC' or 'WSC'.
list(ni,nj) = gf_spmat_get(M,'size') Return a vector where
ni
and nj
are the dimensions of
the matrix.
b = gf_spmat_get(M,'is_complex') Return 1 if the matrix contains complex values.
list(JC, IR) = gf_spmat_get(M,'csc_ind') Return the two usual
index arrays of CSC storage. If M
is not stored as
a CSC matrix, it is converted into CSC.
V = gf_spmat_get(M,'csc_val') Return the array of values of all
non-zero entries of M
. If M
is
not stored as a CSC matrix, it is converted into CSC.
list(N, U0) = gf_spmat_get(M,'dirichlet nullspace',vec R) Solve
the dirichlet conditions M.U=R. A solution
U0
which has a minimum L2-norm is returned, with a
sparse matrix N
containing an orthogonal basis of
the kernel of the (assembled) constraints matrix M
(hence, the PDE linear system should be solved on this subspace):
the initial problem K.U = B with constraints M.U = R is replaced by (N'.K.N).UU = N'.B with U = N.UU + U0
s = gf_spmat_get(M,'info') Return a string contains a short summary on the sparse matrix (dimensions, filling, ...).
gf_spmat_get(M,'save',string format, string filename) Export the sparse matrix. the format of the file may be 'hb' for Harwell-Boeing, or 'mm' for Matrix-Market.