# Ublas :

From: Karl Meerbergen (Karl.Meerbergen_at_[hidden])
Date: 2007-09-04 03:44:42

Regis Behmo wrote:

>On 9/4/07, Karl Meerbergen <Karl.Meerbergen_at_[hidden]> wrote:
>
>
>>Hi,
>>
>>I am not familiar with a mapped_matrix, but for the coordinate_matrix
>>and compressed_matrix, it should be possible to implement a function to
>>remove a row or a column. I think you can easily do this yourself once
>>you are familiar with the format. Why do you use a mapped_matrix?
>>
>>Karl
>>
>>
>
>Bonjour Karl,
>
>I am not familiar with many concepts that are used in the reference
>docs of ublas; therefore, the choice of the mapped_matrix structure is
>probably not entirely justified. I just want to use a sparse matrix
>and I don't know the difference between a mapped_matrix, a
>compressed_matrix and a coordinate_matrix.
>
>Right now, the only way I see to remove a line/column from a sparse
>matrix M (M being either compressed, mapped or coordinate) would be to
>make a copy of M and copy only the elements from the rows/columns
>outside the removed row/column.
>Do you see what I mean?
>Could you give me a clue as how to do it in a more time efficient way?
>
>Régis
>_______________________________________________
>ublas mailing list
>ublas_at_[hidden]

>
>

Hi Regis,

The choice of sparse matrix depends on what you want to do with it.
For example, adding elements in any order is more efficient with
coordinate_matrix than with compressed_matrix.
A matrix times vector product is more efficient with the
compressed_matrix format.

Gunter has a webpage with timings on the choice of format in function of
the algorithm.

All is application dependent: in my finite element applications, I
usually first create the sparse matrix structure in coordinate format,
then create a compressed_matrix from that for use with a matrix vector
product and other algorithms.

The disadvantage of a mapped_matrix is that you are not going to be able
to create matrices of large dimension. I guess this is still the case?

To come to your question of removing a row/column from a sparse matrix:
I would do this by manipulating the index1_data(), index2_data(), and
value_data() of the matrix. But you need to be familiar with sparse
matrix formats to be able to do this swiftly. Unfortunately, I do not
have the time to write such routines for you.

It is rather technical: sparse matrices are more complex to manipulate
than dense ...

Nevertheless, I hope this is helpful.

Karl