[Numpy-discussion] array vs matrix

Anne Archibald peridot.faceted@gmail....
Sat Jun 7 20:54:44 CDT 2008

2008/6/7 Robert Kern <robert.kern@gmail.com>:
> On Sat, Jun 7, 2008 at 14:37, Ondrej Certik <ondrej@certik.cz> wrote:
>> Hi,
>> what is the current plan with array and matrix with regard of calculating
>> sin(A)
>> ? I.e. elementwise vs sin(A) = Q*sin(D)*Q^T? Is the current approach
>> (elementwise for array and Q*sin(D)*Q^T for matrix) the way to go?
> I don't believe we intend to make numpy.matrix any more featureful. I
> don't think it's a good idea for you to base sympy.Matrix's
> capabilities in lock-step with numpy.matrix, though. There are very
> different constraints at work. Please, do what you think is best for
> sympy.Matrix.

Let me elaborate somewhat:

We recently ran across some painful quirks in numpy's handling of
matrices, and they spawned massive discussion. As it stands now, there
is significant interest in reimplementing the matrix object from
scratch, with different behaviour. So emulating its current behaviour
is not a win.

For consistency, it makes a certain amount of sense to have sin(A)
compute a matrix sine, since A**n computes a matrix power. But looking
at the matrix exponential, I see that we have several implementations,
none of which is satisfactory for all matrices. I would expect the
matrix sine to be similar - particularly when faced with complex
matrices - so perhaps needing an explicit matrix sine is a good thing?

Also worth noting is that this problem can be evaded with namespaces;
matrix sin could be scipy.matrixmath.sin, abbreviated perhaps to
mm.sin, as opposed to np.sin.


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