[PYTHON MATRIX-SIG] Standard Numerical Library

[Konrad Hinsen]

   Will 1D functions like the fft (and also 2D functions) work transparently
   on arbitrary multidimensional arrays, along user-specified axes? This
   should be easier to implement efficiently in C than later in Python. 

That reminds me of the long discussion about function ranks a few
months ago. Basically, all functions that act on an array of fixed
rank (1 for 1D FFT, 2 for matrix inversion, etc.) should loop
over the remainining indices of higher-rank arrays. This approach
has proven to be very useful in J.

   This also raises the question of which axis/axes these functions should
   use by default. I find it counter-intuitive that most functions (but not
   all!) default to axis=0 whereas addition, multiplication, etc., match the
   last dimensions of arrays of different depths. Personally, I would prefer
   axis=-1 by default (but there may be a reason against this), or
   axis=(-2,-1) for 2D functions (matrix inversion, etc).

Again the J-style rank approach provides a single rule for sorting
out such things in a very nice way...

   * (Generalised) Cholesky decomposition of symmetric matrices would be very
   useful to anybody dealing with covariance matrices (and I believe it is in
   LINPACK, don't know about LAPACK). 

It's also in LAPACK (everything that is in LINPACK is also in LAPACK,
which was designed as a replacement for LINPACK and EISPACK).

   * Least-squares solution of over/under-determined systems of linear
   equations a la A\b in Matlab.

I agree that all teh features you mentiones are essential for serious
linear algebra work, but I am not sure if they are essential for a
*minimal* standard library. I need pseudoinverses almost daily, but
I also know that many people have never heard of them...

Konrad.

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