gf_spmat_get¶
Synopsis
n = gf_spmat_get(spmat S, 'nnz')
Sm = gf_spmat_get(spmat S, 'full'[, list I[, list J]])
MV = gf_spmat_get(spmat S, 'mult', vec V)
MtV = gf_spmat_get(spmat S, 'tmult', vec V)
D = gf_spmat_get(spmat S, 'diag'[, list E])
s = gf_spmat_get(spmat S, 'storage')
{ni,nj} = gf_spmat_get(spmat S, 'size')
b = gf_spmat_get(spmat S, 'is_complex')
{JC, IR} = gf_spmat_get(spmat S, 'csc_ind')
V = gf_spmat_get(spmat S, 'csc_val')
{N, U0} = gf_spmat_get(spmat S, 'dirichlet nullspace', vec R)
gf_spmat_get(spmat S, 'save', string format, string filename)
s = gf_spmat_get(spmat S, 'char')
gf_spmat_get(spmat S, 'display')
{mantissa_r, mantissa_i, exponent} = gf_spmat_get(spmat S, 'determinant')
Description :
Command list :
n = gf_spmat_get(spmat S, 'nnz')Return the number of non-null values stored in the sparse matrix.
Sm = gf_spmat_get(spmat S, '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(spmat S, '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(spmat S, 'tmult', vec V)Product of M transposed (conjugated if M is complex) with the vector V.
D = gf_spmat_get(spmat S, '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(spmat S, 'storage')Return the storage type currently used for the matrix.
The storage is returned as a string, either ‘CSC’ or ‘WSC’.
{ni,nj} = gf_spmat_get(spmat S, 'size')Return a vector where ni and nj are the dimensions of the matrix.
b = gf_spmat_get(spmat S, 'is_complex')Return 1 if the matrix contains complex values.
{JC, IR} = gf_spmat_get(spmat S, '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(spmat S, '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.
{N, U0} = gf_spmat_get(spmat S, '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
gf_spmat_get(spmat S, '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.
s = gf_spmat_get(spmat S, 'char')Output a (unique) string representation of the spmat.
This can be used to perform comparisons between two different spmat objects. This function is to be completed.
gf_spmat_get(spmat S, 'display')displays a short summary for a spmat object.
{mantissa_r, mantissa_i, exponent} = gf_spmat_get(spmat S, 'determinant')returns the matrix determinant calculated using MUMPS.
