scipy.optimize.fmin_cg¶
- scipy.optimize.fmin_cg(f, x0, fprime=None, args=(), gtol=1e-05, norm=inf, epsilon=1.4901161193847656e-08, maxiter=None, full_output=0, disp=1, retall=0, callback=None)[source]¶
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), optional
Function which computes the gradient of f.
args : tuple, optional
Extra arguments passed to f and fprime.
gtol : float, optional
Stop when norm of gradient is less than gtol.
norm : float, optional
Order of vector norm to use. -Inf is min, Inf is max.
epsilon : float or ndarray, optional
If fprime is approximated, use this value for the step size (can be scalar or vector).
callback : callable, optional
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.
See also
- minimize
- Interface to minimization algorithms for multivariate functions. See the ‘CG’ method in particular.
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.
