[SciPy-user] projection of a point to a set defined by linear constraints

[Dmitrey]

hi all,
Suppose I have set of linear constraints
b1 <= Ax <= b2
and point X = [x1, ..., xn] out of the set.

A is n x m matrix, b1, b2 are vectors of length m (some of b2, b1
coords can be +/- inf or equal).

What is the best way to find projection of the point x to the
set? I.e. ||X-x||_2 -> min, s.t. b1 <= Ax <= b2.

I intended to use constrained LLSP solver,
something like ACM TOMS 587

But:
 * Using f2py yields error, as I have mentioned here
   
http://groups.google.com/group/scipy-user/browse_thread/thread/bb3ad277e9213d3e

 * Since the problem has eye matrix (||Cx-d||^2 -> min, C = I), I
thought maybe there are more efficient ways (and/or software) to do
it?

Could you recommend me another forum/google group where I should
search, an article or mb some code (preferably Python or MATLAB)?

I was recommended to involving ODRPack and I know it is included into 
scipy, can this somehow help?
BTW I intend to involve it to speedup my NLP/NSP sover ralg for problems 
with lots of constraints, other than only box-bounded ones.

Thank you in advance,
D.