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looked into how to compute the RREF of big moderately sparse matrices (especially over GF(2)):  looked into how to compute the RREF of big sparse to moderately dense matrices (especially over GF(2)): 
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* need more experimental data of matrices that we want to efficiently reduce: generating them  * need more experimental data of matrices that I am really interested in: generating them 
what I did this week
looked into how to compute the RREF of big sparse to moderately dense 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 I am really interested in: generating them