scipy.optimize.fmin_ncg¶
- scipy.optimize.fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1.0000000000000001e-05, epsilon=1.4901161193847656e-08, maxiter=None, full_output=0, disp=1, retall=0, callback=None)¶
Minimize a function using the Newton-CG method.
Parameters: - f : callable f(x,*args)
Objective function to be minimized.
- x0 : ndarray
Initial guess.
- fprime : callable f’(x,*args)
Gradient of f.
- fhess_p : callable fhess_p(x,p,*args)
Function which computes the Hessian of f times an arbitrary vector, p.
- fhess : callable fhess(x,*args)
Function to compute the Hessian matrix of f.
- args : tuple
Extra arguments passed to f, fprime, fhess_p, and fhess (the same set of extra arguments is supplied to all of these functions).
- epsilon : float or ndarray
If fhess is approximated, use this value for the step size.
- callback : callable
An optional user-supplied function which is called after each iteration. Called as callback(xk), where xk is the current parameter vector.
Returns: (xopt, {fopt, fcalls, gcalls, hcalls, warnflag},{allvecs})
- xopt : ndarray
Parameters which minimizer f, i.e. f(xopt) == fopt.
- fopt : float
Value of the function at xopt, i.e. fopt = f(xopt).
- fcalls : int
Number of function calls made.
- gcalls : int
Number of gradient calls made.
- hcalls : int
Number of hessian calls made.
- warnflag : int
Warnings generated by the algorithm. 1 : Maximum number of iterations exceeded.
- allvecs : list
The result at each iteration, if retall is True (see below).
Other Parameters:
- avextol : float
- Convergence is assumed when the average relative error in the minimizer falls below this amount.
- maxiter : int
- Maximum number of iterations to perform.
- full_output : bool
- If True, return the optional outputs.
- disp : bool
- If True, print convergence message.
- retall : bool
- If True, return a list of results at each iteration.
Notes: - scikits.openopt offers a unified syntax to call this and other solvers.
2. Only one of fhess_p or fhess need to be given. If fhess is provided, then fhess_p will be ignored. If neither fhess nor fhess_p is provided, then the hessian product will be approximated using finite differences on fprime. 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.
