set efficently specific rows in a sparse matrix to 0

Hello comunity,

I have a sparse matrix J and i want to set specific rows to 0.

My code's like this:

J = sparse(blkdiag(rand(1000,1000),rand(100,100),rand(200,200))); % just for example

vec = [1:10,20:400,800:1100]';   % just for example

J(vec,:) = 0;

Is there a faster way how to set the specific lines of J to 0?

best regards,

Marko

Answer 1

[Walter Roberson]

Would it be practical in your situation to transpose the matrix, so that you were setting columns to 0 instead of rows? With the implementation of sparse(), it is more efficient to change multiple items in one column.

Beyond that: the usual efficiency hint is to find() the non-zero objects of interest, and then use those indices to assign new values.

It would be interesting to benchmark,

J(vec,:) = 0;

compared to

[r,c] = find(J(vec,:));
J(sub2ind(size(J), vec(r), c)) = 0;

compared to

[r,c,s] = find(J(vec,:));
J = J - sparse(vec(r), c, s, size(J,1), size(J,2));

The third of those is locating the non-zero elements of those rows of J, along with the values there, and building a new sparse array that contains only those elements, and then subtracts the two.

Answer 2

[Marko]

here are the benchmark results:

    ver    Var2 
    ___    _____
     1     2.242
     2       NaN
     3      0.87

for version 2 i get the error:

Error using sub2ind (line 51)
The subscript vectors must all be of the same size.
Error in setBC (line 12)
J(sub2ind(size(L), vec(r), c)) = 0;

so your code snippet (version 3) saved me 61% or 1,3s every time i ran a computation.

Thank you very much

[Walter Roberson]

In your posted question, vec is a column vector. And the code I posted works when vec is a column vector. But if vec were a row vector instead then you would get the error you mention.