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 * learned that this is a hard problem

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

dev1/rpw (last edited 2008-11-14 13:42:07 by localhost)