Clear message

what I did this week

looked into how to compute the RREF of big moderately sparse matrices (especially over GF(2)):

• learned that this is a hard problem
• looked into Macaulay F4 implementation, because they seem to do sparse elimination. got excited/depressed about its speed for Cyclic14 over GF(2) at a certain point, then found out that implementation is buggy and generated incorrect results
• paired down code for structured Gaussian elimination, writing wrappers for this
• at a certain point was not sure whether it would be easier to write wrappers or rewrite the code in Python/Cython
• compressed matrix representations (deflate() each row): didn't get very far with that, deflate() generates a significant performance impact.
• need more experimental data of matrices that we want to efficiently reduce: generating them