Metadata-Version: 1.1
Name: pytron
Version: 0.1
Summary: Python bindings for TRON optimizer
Home-page: http://pypi.python.org/pypi/pytron
Author: Fabian Pedregosa
Author-email: f@fabianp.net
License: Simplified BSD
Description: A Trust-Region Newton Method in Python
        ======================================
        
        The main function is pytron.minimize::
        
            def minimize(func, grad_hess, x0, args=(), max_iter=1000, tol=1e-6):
        
                Parameters
                ----------
                func : callable
                    func(w, *args) is the evaluation of the function at w, It
                    should return a float.
                grad: callable
                    grad(w, *args) is the gradient of func at w, it
                    should return a numpy array of size x0.size
                hess: callable
                    hess(w, s, *args) returns the dot product H.dot(s), where
                    H is the Hessian matrix at w. It must return a numpy array
                    of size x0.size
                tol: float
                    stopping criterion. XXX TODO. what is the stopping criterion ?
        
                Returns
                -------
                w : array
        
        
        
        Stopping criterion
        ------------------
        
        It stops whenever ||grad(x)|| < eps or the maximum number of iterations is
        attained.
        
        TODO: add tol
        
        Examples
        --------
        
        Code
        ----
        This software uses the `TRON optimization software
        <http://www.mcs.anl.gov/~more/tron/>`_  (files src/tron.{h,cpp}) that was
        taken from LIBLINEAR 1.93 (BSD licensed).
        
        The modifications with respect to the orginal code are:
        
            * Do not initialize values to zero, allow arbitrary initializations
        
            * Modify stopping criterion to comply with scipy.optimize API. Stop
              whenever gradient is smaller than a given quantity, specified in the
              gtol argument
        
            * Add the gradient to TRON::tron
        
        
        References
        ----------
        If you use the software please consider citing some of the references below.
        
        The method is described in the paper "Newton's Method for Large
        Bound-Constrained Optimization Problems", Chih-Jen Lin and Jorge J. Moré
        (http://epubs.siam.org/doi/abs/10.1137/S1052623498345075)
        
        It is also discussed in the contex of Logistic Regression in the paper "Trust
        Region Newton Method for Logistic Regression", Chih-Jen Lin, Ruby C. Weng,
        S. Sathiya Keerthi (http://dl.acm.org/citation.cfm?id=1390703)
        
        The website http://www.mcs.anl.gov/~more/tron/ contains reference to this
        implementation, although the links to the software seem to be currently
        broken (May 2013).
        
        
        License
        -------
        This code is licensed under the terms of the BSD license. See file COPYING
        for more details.
        
        
        Acknowledgement
        ---------------
        The source code for the
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Topic :: Software Development
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Requires: numpy
Requires: scipy
