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)

Minimize a function using the BFGS algorithm.

Parameters : Returns : f : callable f(x,*args) Objective function to be minimized. x0 : ndarray Initial guess. fprime : callable f’(x,*args) Gradient of f. args : tuple Extra arguments passed to f and fprime. gtol : float Gradient norm must be less than gtol before succesful termination. norm : float Order of norm (Inf is max, -Inf is min) epsilon : int or ndarray If fprime is approximated, use this value for the step size. callback : callable An optional user-supplied function to call after each iteration. Called as callback(xk), where xk is the current parameter vector. 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. 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.

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.

Previous topic

scipy.optimize.fmin_cg

Next topic

scipy.optimize.fmin_ncg