[Numpy-discussion] ANNOUNCE: New algorithms added to optimize.py
Travis Oliphant
Oliphant.Travis at mayo.edu
Thu Aug 31 14:20:22 CDT 2000
This is a heads up to those interested in Python-implemented optimization
algorithms.
I've updated my optimize.py module to version 0.3 by including two new
unconstrained optimization algorithms to minimize a function of many
variables -- one which implements a quasi-Newton algorithm (BFGS) and
another which implements a practical Newton's algorithm using conjugate
gradients to invert the Hessian (approximated if not provided).
The license is very liberal it's available at
http://oliphant.netpedia.net/packages/optimize.py
Here are the docstrings:
>>> print optimize.fminBFGS.__doc__
xopt = fminBFGS(f, fprime, x0, args=(), avegtol=1e-5,
maxiter=None, fulloutput=0, printmessg=1)
Optimize the function, f, whose gradient is given by fprime using the
quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS)
See Wright, and Nocedal 'Numerical Optimization', 1999, pg. 198.
>>> print optimize.fminNCG.__doc__
xopt = fminNCG(f, fprime, x0, fhess_p=None, args=(), avextol=1e-5,
maxiter=None, fulloutput=0, printmessg=1)
Optimize the function, f, whose gradient is given by fprime using the
Newton-CG method. fhess_p must compute the hessian times an arbitrary
vector. If it is not given, finite-differences on fprime are used to
compute it. See Wright, and Nocedal 'Numerical Optimization', 1999,
pg. 140.
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