On Sep 6, 5:15 pm, Vinzent Steinberg
<vinzent.steinb...@googlemail.com> wrote:
> On Sep 5, 10:29 pm, Bryan <alke...@gmail.com> wrote:
>
> > Thats exactly what I am trying to do, solve symbolic matrices which
> > have numeric coefficients along symbols, thus I would like the numeric
> > coefficients to be treated as floats to speed things up a bit.
>
> > The documentation says that it is generally the case that exact
> > arithmetic
> > is used, so I was wondering how to tap into the non-general case
> > of having my numbers treated as floats.
>
> > But it appears to me that this is just the case of misleading
> > documentation
> > then?
>
> Please feel free to improve our documentation. :)
>
> Actually it is quite easy to mix symbols and floating point
> arithmetic, just use sympy.Float for your coefficients:
>
> >>> from sympy import *
> >>> var('a:d')
> (a, b, c, d)
> >>> m = Matrix([[Float('0.1')*a, Float('0.2')*b], [Float('0.3')*c,
> >>> Float('0.4')*d]])
> >>> m
>
> [0.1*a, 0.2*b]
> [0.3*c, 0.4*d]
>
> I'm not sure that it will be much faster though.
Of course you get the problem that the algorithm is probably not
numerically stable.
Vinzent
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