Hi Thierry, The sparse matrix actually uses a multimodular algorithm in this case. It's written in Cython but essentially runs at Python speed. However, that's not where the slowness comes from. Even when running the dense matrix with that same algorithm, it's still very fast.
I agree that fundamentally sparse matrices cannot be as fast. But in my experience they are often also not very optimized in SageMath. But here it seems that something is actually broken because what takes so long is computing the transpose of the input matrix. (To reduce a solve_left() to a solve_right().) SageMath seems to have trouble with sparse matrices with many columns (actually not that many.) The underlying issue seems to be that this already takes 6 seconds on my machine (and this is one order of magnitude smaller than the example from Peter.): A = matrix(QQ, 1, 10000, range(10000), sparse=True) If somebody wants to dig deeper, I am attaching some profiler output that can be opened with https://www.speedscope.app/. julian -- You received this message because you are subscribed to the Google Groups "sage-support" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/sage-support/b57bbc69-7938-4a84-ac1c-d63ac32f1a6en%40googlegroups.com.
sparse.speedscope.json
Description: application/json
