Hi, I'm in the process of writing a Python linear

algebra module.

The current targeted interface is:

http://oregonstate.edu/~barnesc/temp/linalg/
The interface was originally based on Raymond

Hettinger's

Matfunc [1]. However, it has evolved so that now it

is

nearly identical to JAMA [2], the Java matrix library.

I am soliticing comments on this interface.

Please post up any criticism that you have. Even

small

things -- if something isn't right, it's better to fix

it now than later.

I have not made source code available yet, since the

current code is missing the decompositions and doesn't

match the new interface. I'm in the process of

rewritting the code to match the new interface. You

can e-mail me and ask for the old code if you're

curious

or skeptical.

[1].

http://users.rcn.com/python/download/python.htm
[2].

http://math.nist.gov/javanumerics/jama/
---------------------------------------------

Brief comparison with Numeric

---------------------------------------------

Numeric and linalg serve different purposes.

Numeric is intended to be a general purpose array

extension. It takes a "kitchen sink" approach,

and includes every function which could potentially

be useful for array manipulations.

Linalg is intended to handle real/complex vectors

and matrices, for scientific and 3D applications.

It has a more restricted scope. Because it is

intended for 3D applications, it is optimized

for dimension 2, 3, 4 operations.

For the typical matrix operations, the linalg

interface is much intuitive than Numeric's. Real

and imaginary components are always cast to

doubles, so no headaches are created if a matrix

is instantiated from a list of integers. Unlike

Numeric, the * operator performs matrix

multiplication, A**-1 computes the matrix inverse,

A == B returns True or False, and the 2-norm and

cross product functions exist.

As previously stated, linalg is optimized for

matrix arithmetic with small matrices (size 2, 3, 4).

A (somewhat out of date) set of microbenchmarks [3]

[4]

show that linalg is roughly an order of magnitude

faster than Numeric for dimension 3 vectors and

matrices.

[3].

Microbenchmarks without psyco:

http://oregonstate.edu/~barnesc/temp/
numeric_vs_linalg_prelim-2005-09-07.pdf

[4].

Microbenchmarks with psyco:

http://oregonstate.edu/~barnesc/temp/
numeric_vs_linalg_prelim_psyco-2005-09-07.pdf

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