This is a locol minimization algorithm. Uses a quasi-Newton method with BFGS update of the estimated inverse hessian. It is robust against certain pathologies common on likelihood functions. It attempts to be robust against "cliffs", i.e. hyperplane discontinuities, though it is not really clear whether what it does in such cases succeeds reliably.

This algorithm is somewhat more robust, apparently, than the stock optimization programs that do about the same thing. The minimizer can negotiate discontinuous "cliffs" without getting stuck.

All the functions in csminwel.py were created by Christopher Sims in 1996. They are my translations to python from the author's original MATLAB files.

The originals can be found in: http://sims.princeton.edu/yftp/optimize/

I kept the author's original variable names, so it is easier to compare this code with his. Also, this code is not very pythonic, it is very matlaby. I need a better understanding of the algorithm before making it more pythonic.