We should be able to do even better, right?

Generate upper triangular + lower triangular matrix and do a product?


On Monday 02 Jul 2012, charles Bouillaguet wrote:
> Hi,
> 
> I was wondering why some of my code was Dawn Slow (tm), and I ended up
> being surprised to notice that it was spending all its time trying to
> generate a random invertible matrix.... In particular, over finite
> fields, GL(N, GF(q)).random_element() is MUCH MUCH MUCH slower than
> the naive method that just generates a random matrix, checks if it is
> invertible, and tries again if it is not the case
> 
> 
> sage: %time GL(64, GF(2)).random_element()
> CPU times: user 20.47 s, sys: 2.64 s, total: 23.11 s
> Wall time: 28.93 s
> 
> --> 30s is not a reasonable performance to generate a small random
> invertible matrix....
> 
> By the way, this fails with large primes.
> 
> %time GL(64, GF(2^127-1)).random_element()
> ....
> TypeError: Unable to convert Gap element 'ZmodpZObj(
> 152551219330529388046437174479921365258,
> 170141183460469231731687303715884105727 )'
> error coercing to finite field
> 
> I would suggest overloading GL(N, K).random_element() with the naive
> procedure when K is a finite field.
> 
> def faster_random_invertible_matrix(n,K):
>     S = matrix(K,n)
>     while not S.is_invertible():
>         S = MatrixSpace(K,n,n).random_element()
>     return S
> 
> 
> sage: %timeit faster_random_invertible_matrix(64, GF(2))
> 125 loops, best of 3: 1.8 ms per loop
> 
> ---> this is about 15000 times faster....
> 
> How do you feel about this ? It's not a bug stricto sensu, but
> math-oriented people might stumble across GL(N,K).random_element() and
> try to use it even is a much faster solution is available.
> --
> Charles Bouillaguet

Cheers,
Martin

--
name: Martin Albrecht
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