gf_spmat_getΒΆ

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.

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