Class FminCG

source code

 object --+    
          |    
FminWrapper --+
              |
             FminCG

Abstract class to generate wrappers around scipy.optimize fmin_*
functions.

Parameters
-----------

criterion : Criterion
    A criterion function with __call__ and gradient methods.
x0 : ndarray (None)
    First guess
args=() : tuple
    Extra arguments for the criterion function
kwargs : dict
    Parameters of the fmin_function

fmin function docstring
------------------------
Minimize a function using a nonlinear conjugate gradient algorithm.

Parameters
----------
f : callable f(x,*args)
    Objective function to be minimized.
x0 : ndarray
    Initial guess.
fprime : callable f'(x,*args)
    Function which computes the gradient of f.
args : tuple
    Extra arguments passed to f and fprime.
gtol : float
    Stop when norm of gradient is less than gtol.
norm : float
    Order of vector norm to use.  -Inf is min, Inf is max.
epsilon : float or ndarray
    If fprime is approximated, use this value for the step
    size (can be scalar or vector).
callback : callable
    An optional user-supplied function, called after each
    iteration.  Called as callback(xk), where xk is the
    current parameter vector.

Returns
-------
xopt : ndarray
    Parameters which minimize f, i.e. f(xopt) == fopt.
fopt : float
    Minimum value found, f(xopt).
func_calls : int
    The number of function_calls made.
grad_calls : int
    The number of gradient calls made.
warnflag : int
    1 : Maximum number of iterations exceeded.
    2 : Gradient and/or function calls not changing.
allvecs : ndarray
    If retall is True (see other parameters below), then this
    vector containing the result at each iteration is returned.

Other Parameters
----------------
maxiter : int
    Maximum number of iterations to perform.
full_output : bool
    If True then return fopt, func_calls, grad_calls, and
    warnflag in addition to xopt.
disp : bool
    Print convergence message if True.
retall : bool
    Return a list of results at each iteration if True.

Notes
-----
Optimize the function, f, whose gradient is given by fprime
using the nonlinear conjugate gradient algorithm of Polak and
Ribiere. See Wright & Nocedal, 'Numerical Optimization',
1999, pg. 120-122.