FYI, my SciPy talk for SymPy was not accepted (it was accepted for the
poster session). My talk on conda was accepted, as was the SymPy
tutorial.

Aaron Meurer

On Tue, Apr 1, 2014 at 4:58 PM, Ondřej Čertík <ondrej.cer...@gmail.com> wrote:
> On Mon, Mar 31, 2014 at 9:14 PM, Matthew Rocklin <mrock...@gmail.com> wrote:
>> http://www.evanmiller.org/mathematical-hacker.html
>>
>> I reference that blog post pretty often.  I fully intend to reference it
>> again in my talk (if it is accepted).
>>
>> The interesting thing about the Factorial / Gamma / loggamma example is that
>> to find the solution you need to find someone who knows both that n! =
>> Gamma(n+ 1) and who knows that a loggamma routine is commonly found in lower
>> level languages.  Those bits of information are usually held by different
>> experts.  Ondrej said "Of course, that's obvious" when I first reposted the
>> article on G+.
>
> That's funny, I forgot that I said that and just had the same reaction.
>
> However, we are still missing rational function approximation in SymPy
> or mpmath.
> That's what is used to implement things like log_gamma or erf (error
> function) in
> Fortran or C. So I have just created an issue for it:
>
> https://github.com/sympy/sympy/issues/7359
>
> and I spent time explaining exactly how it works in it and giving examples,
> including for example the implementation of erf() in gfortran.
>
> Aaron, you were asking about examples of numerical cancellation. I
> worked out one in the
> issue as well.
>
> Ondrej
>
>>
>> You're right that this is similar to my last talk.  The last one though was
>> mostly about an application (numerical linear algebra).  I actually want to
>> talk a bit more about the philosophy and some of the more abstract tools
>> that people might actually use.  Your first impression is a valuable one
>> though, I should go through my last talk and make sure that I'm not
>> repeating too much that shouldn't be repeated.
>>
>>
>> On Mon, Mar 31, 2014 at 6:03 PM, Aaron Meurer <asmeu...@gmail.com> wrote:
>>>
>>> That's a good point. One of the nicest things about symbolics, when you
>>> can get it, is that it can make things drastically more efficient by doing
>>> mathematical simplifications. Evaluating integrals symbolically is a nice
>>> example of this (especially for SymPy, which has some pretty nice algorithms
>>> to compute definite integrals).
>>>
>>> I'm reminded of a popular blog post (I can't find a link right now) about
>>> how know math is important for programmers. It has the example of how all
>>> these programming languages show how they they compute factorial, and how
>>> tail recursion can make it linear or whatever, but the actual best way to
>>> compute it is to use loggamma, which gives the answer in constant time.
>>>
>>> Aaron Meurer
>>>
>>>
>>> On Mon, Mar 31, 2014 at 7:51 PM, Tim Lahey <tim.la...@gmail.com> wrote:
>>>>
>>>>
>>>>
>>>> On 31 Mar 2014, at 20:29, Aaron Meurer wrote:
>>>>
>>>>> On Mon, Mar 31, 2014 at 11:32 AM, Matthew Rocklin
>>>>> <mrock...@gmail.com>wrote:
>>>>>
>>>>>> I like that you emphasized the utility for numerics, I think that this
>>>>>> is
>>>>>> likely to be a selling point for the SciPy crowd.
>>>>>>
>>>>>
>>>>> Yes, this was very intentional. I may need some help gathering up some
>>>>> nice
>>>>> motivating examples if this is accepted.
>>>>
>>>>
>>>> One motivating example for me is the integration of products of functions
>>>> over areas and volumes. For finite elements, you'll get products of pairs 
>>>> of
>>>> trial functions (usually polynomials). It's even more useful for products 
>>>> of
>>>> trig functions. Performing the integration of any of theses is easy enough
>>>> with numerical integration, but it's much more efficient to calculate the
>>>> integrals symbolically and then perform the evaluation for each element.
>>>>
>>>> Cheers,
>>>>
>>>> Tim.
>>>>
>>>>
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