Greetings. This might not qualify as interesting or relevant, but for
my own research, I wrote a Sage app to calculate via Monte Carlo
approximation the Cramer-Rao bounds for estimating certain target
parameters from synthetic aperture radar data. MPMath's arbitrary-
precision float functionality and quadrature integration routines were
essential (I had integrands needing 30-200 decimal digits of
precision). The @parallel decorator worked fantastically on our 4- and
8-core boxes. The app used Numpy as well. We haven't published results
from this yet but will in the coming few months.

MPMath (and it being nicely packaged in Sage via SymPy) made this
application possible---I couldn't find support for high-precision math
in Matlab so I can't really benchmark its speed. There may be better
ways to solve this problem than the method I chose (Monte Carlo +
numerical integration) so it might not be of interest to SIAM but it
has helped us answer some of our research questions.


On Mar 16, 2:21 am, Rob Beezer <[email protected]> wrote:
> Sage-Devel,
>
> I've been asked to write a review of Sage for SIAM Review, the main
> journal for the Society for Industrial and Applied Mathematics.  I'd
> like to include some concrete examples of some fast and powerful
> commands in Sage that would interest applied mathematicians.  For
> example, Sage can do the determinant of a size 400 matrix with integer
> entries in about 1 second on inexpensive hardware.  Can you contribute
> a favorite example like this, but which would get the attention of the
> readers of SIAM Review (i.e. not exact linear algebra)?    If you send
> me exact code I can get timings all from the same machine.
>
> Also, which packages should I highlight?  For example: BLAS, LAPACK,
> CVXOPT, GSL, MPFR, Scipy, Numpy, or ?
>
> Thanks in advance.
>
> Rob

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