2021-10-13 17:19:11 +0200 received badge ● Popular Question (source) 2018-12-24 03:29:45 +0200 commented answer NEW VERSION: Sparse matrix stores something for each row? @nbruin OK, thank you for the insight (as you can see though, I really need matrix multiplication, so it is not clear how I'd make use of the polynomials trick)... But am I crazy or did you just edit my comment, deleting most of it (including my response to @rburing) and replacing the deleted part with a response to me? No offense taken, but could you please undo that? 2018-12-23 17:10:20 +0200 commented answer NEW VERSION: Sparse matrix stores something for each row? @nbruin I am certainly no expert on sparse matrix algorithms but I still feel like this is a questionable choice for the default (and only) format. with apologies of nbruin: It looks like I edited this comment when I was intending to reply. If someone can find the history of this comment and restore it in its old form, that would be great. 2018-12-22 02:04:34 +0200 commented answer NEW VERSION: Sparse matrix stores something for each row? @tmonteil Yes, I've seen the question and the ticket and they are somewhat related. This is a separate issue though, a memory efficiency issue rather than time efficiency. Plus it concerns not only multiplication but construction of sparse matrices in general, which I realized after posting and tried to explain in the second update. Maybe I should make that clear at the very top. 2018-12-19 13:32:21 +0200 received badge ● Editor (source) 2018-12-19 11:17:53 +0200 received badge ● Student (source) 2018-12-19 11:15:03 +0200 asked a question NEW VERSION: Sparse matrix stores something for each row? NEW VERSION. This is what this question has boiled down to upon further investigation. Everything below this paragraph in boldface and the subsequent code is the old version and serves purely historical purposes =) I hope this was the right thing to do rather than creating a new question. The issue is that the amount of memory occupied by a sparse matrix seems to depend linearly on the number of rows in the matrix. In particular, my setup wont even let me create a zero $10^9\times 1$ sparse matrix but has no problem with a zero $1\times 10^9$ sparse matrix. This can all be illustrated by running the below code (and it's hardly the way things are supposed to be). print get_memory_usage() B=matrix(QQ, 1, 1000000000, {}) print get_memory_usage() B=matrix(QQ, 10000000, 1, {}) print get_memory_usage() B=matrix(QQ, 1000000000, 1, {}) print get_memory_usage()  I'm running the following code. Here I create a sparse $10^5\times 10^5$ identity matrix and then apply it repeatedly to a vector in $\mathbb R^{10^5}$ with 100 nonzero coordinates (which is stored as $10^5\times 1$ sparse matrix). Each time I add the result to a list until I run out of 2 GB of memory. A=matrix(QQ, 100000, 100000, {(i,i):1. for i in range(100000)}) print get_memory_usage() B=[matrix(QQ, 100000, 1, {(i,0):1. for i in range(100)})] while (get_memory_usage()<2000): B.append(A*B[-1]) print len(B) print get_memory_usage() del B del A print get_memory_usage()  I'm receiving (on a freshly started kernel) 204.54296875 196 2003.51953125 1399.0234375  This raises two questions. Why is there so much memory (1.4 GB) still in use after I successfully ran the code and deleted both objects I've created? That's a leak, right? Why does deleting a list of 196 sparse matrices with 100 nonzero elements each free up 600 MB? Each such matrix should only take up a few KB, right? I'm on Windows 8.1/SAGE 8.4. UPDATE. Transposing the matrices, i.e. writing ... B=[matrix(QQ, 1, 100000, {(0,i):1. for i in range(100)})] for i in range(200): B.append(B[-1]*A) ...  seems to work well memory-wise, it returns 214.50390625 201 215.53125 186.62109375  However, it takes up much more time than the first version. This is probably due to the implementation of sparse matrix multiplication unfortunately containing a loop over the columns of the right matrix. Is there any simple way around this high memory/low speed dilemma? UPDATE 2. This might not be a memory leak and have more to do with the implementation of sparse matrices in general rather than their multiplication in particular. Apparently, a sparse matrix stores something for each of its rows as shown by print get_memory_usage() B=matrix(QQ, 1, 10000000, {}) print get_memory_usage() B=matrix(QQ, 10000000, 1, {}) print get_memory_usage() 165.45703125 165.51953125 1082.68359375  This has got to be a known issue. I was not able to find a discussion, however. (This might be what is known as csr_matrix in scipy but why this would be chosen as the general standard here is beyond me.)