Robust statistics and optimmization from Python

[Robert Kern]

tkpmep at hotmail.com wrote:
> I use Python to generate a huge amount of data in a .csv file which I
> then process using Excel. In particular, I use Excel's solver to solve
> a number of non-linear equation, and then regress the results of
> hundreds of calls to Solver against a set of known values, enabling me
> to calibrate my model. This is a pain: i'd much rather perform all the
> computations in Python and improve on Excels' regression as well.
> 
> Questions:
> 1. Is there a way to perform (or make a call to) a non-linear
> optimization from Python?

Look at scipy. http://www.scipy.org

In [1]: from scipy import optimize

In [2]: optimize?
...
    Optimization Tools
    ==================

     A collection of general-purpose optimization routines.

       fmin        --  Nelder-Mead Simplex algorithm
                         (uses only function calls)
       fmin_powell --  Powell's (modified) level set method (uses only
                         function calls)
       fmin_cg     --  Non-linear (Polak-Rubiere) conjugate gradient
algorithm
                         (can use function and gradient).

       fmin_bfgs   --  Quasi-Newton method (can use function and gradient)
       fmin_ncg    --  Line-search Newton Conjugate Gradient (can use
                         function, gradient and hessian).
       leastsq     --  Minimize the sum of squares of M equations in
                         N unknowns given a starting estimate.

      Constrained Optimizers (multivariate)

       fmin_l_bfgs_b -- Zhu, Byrd, and Nocedal's L-BFGS-B constrained
optimizer
                          (if you use this please quote their papers --
see help)

       fmin_tnc      -- Truncated Newton Code originally written by
Stephen Nash and
                          adapted to C by Jean-Sebastien Roy.

       fmin_cobyla   -- Contrained Optimization BY Linear Approximation

> 2. Do Python packages for robust statistics (robust regression in
> particular) exist. If so, which one would you recommend/

Offhand, I can't think of any, but it's easy enough to do maximum
likelihood with Laplacians and the functions above. If you find suitable
FORTRAN or C code that implements a particular "robust" algorithm, it
can probably wrapped for scipy relatively easily.

-- 
Robert Kern
rkern at ucsd.edu

"In the fields of hell where the grass grows high
 Are the graves of dreams allowed to die."
  -- Richard Harter