scipy.optimize.fmin_bfgs¶
- scipy.optimize.fmin_bfgs(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 the BFGS algorithm.
Parameters : f : callable f(x,*args)
Objective function to be minimized.
x0 : ndarray
Initial guess.
fprime : callable f’(x,*args), optional
Gradient of f.
args : tuple, optional
Extra arguments passed to f and fprime.
gtol : float, optional
Gradient norm must be less than gtol before succesful termination.
norm : float, optional
Order of norm (Inf is max, -Inf is min)
epsilon : int or ndarray, optional
If fprime is approximated, use this value for the step size.
callback : callable, optional
An optional user-supplied function to call 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.
gopt : ndarray
Value of gradient at minimum, f’(xopt), which should be near 0.
Bopt : ndarray
Value of 1/f’‘(xopt), i.e. the inverse hessian matrix.
func_calls : int
Number of function_calls made.
grad_calls : int
Number of gradient calls made.
warnflag : integer
1 : Maximum number of iterations exceeded. 2 : Gradient and/or function calls not changing.
allvecs : list
Results at each iteration. Only returned if retall is True.
Other Parameters: maxiter : int
Maximum number of iterations to perform.
full_output : bool
If True,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 ‘BFGS’ method in particular.
Notes
Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS)
References
Wright, and Nocedal ‘Numerical Optimization’, 1999, pg. 198.
