If you take a look at the source of numpy's linalg.py, you'll see that
solves uses dgesv /zgesv for real /complex solves. If you Google dgesv, you
get:
DGESV computes the solution to a real system of linear equations
A * X = B,
where A is an N-by-N matrix and X and B are N-by-NRHS matrices.
The LU decomposition with partial pivoting and row interchanges is
used to factor A as
A = P * L * U,
where P is a permutation matrix, L is unit lower triangular, and U is
upper triangular. The factored form of A is then used to solve the
system of equations A * X = B.
Don't take my word for it though; that was just the first google hit I
found. Also, I don't know if scipy solve differs from numpy.solve here, nor
which you are using, so I recommend that you repeat the exercise on your
own.
-tim
On 9/21/07, mark <[EMAIL PROTECTED]> wrote:
>
> Hello, anybody know what approach is used in linalg.solve?
>
> I used it in a paper and some reviewer wants to know.
>
> Some Gaussian elimination with pivoting or something more fancy?
>
> Thanks,
>
> Mark
>
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