On Fri, Mar 14, 2014 at 4:26 PM, Thilina Rathnayake
<[email protected]> wrote:
> Thank you Ondrej and Tim for your inputs. I too think we should concentrate
> on Symbolic matrix manipulation rather than the numerical matrices since
> there
> are good libraries we can use to do them.
>
> The approach used in Armadillo is that if the user is willing to install
> additional
> libraries it uses them for fast calculations, otherwise it uses it's own
> implementation.

That's generally the best, e.g. for integer factorization, we
implemented a simple
and reasonable fast algorithm ourselves and use optional libraries for
(much) faster
execution if the user enables them.

> If we were to just throw an exception in this kind of a scenario, certain
> other modules
> which user might think as unrelated, might have to be disabled too.

Yes, and also one has to disable/enable our tests. So it's better to implement
a simple implementation ourselves.

>
> For example, Suppose CSymPy has an equation solver which solves a system
> of equations using matrix manipulations. If we don't have our own
> implementation
> of the matrices and if the user didn't install it through an external
> library, it won't be
> possible for the user to use the equation solver.

Right.

>
> If the goal of CSymPy is to be a symbolic manipulation library, I don't
> think the above is
> going to be a big problem. I see lots of pros in concentrating just on
> symbolic calculation.
> For example, if we do a good job on symbolic manipulation rather than the
> current general
> CASs, CSymPy with a good numerical library like numpy might be an unbeatable
> combination.

That's right. I see it to be used in combination with SymPy and other
Python libraries
to obtain nice environment. Btw, there is also this new NumPy like
library in C++:

https://github.com/ContinuumIO/libdynd

I don't follow it too closely, but I think it follows the same concept
as CSymPy --- stick to
pure C++, and provide optional Python wrappers.

Ondrej

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