_minimize_bfgs(fun, x0, args=(), jac=None, callback=None, gtol=1e-05, norm=inf, eps=1.4901161193847656e-08, maxiter=None, disp=False, return_all=False, finite_diff_rel_step=None, xrtol=0, **unknown_options)
Set to True to print convergence messages.
Maximum number of iterations to perform.
Terminate successfully if gradient norm is less than gtol.
Order of norm (Inf is max, -Inf is min).
If jac is None the absolute step size used for numerical approximation of the jacobian via forward differences.
Set to True to return a list of the best solution at each of the iterations.
If jac in ['2-point', '3-point', 'cs'] the relative step size to use for numerical approximation of the jacobian. The absolute step size is computed as h = rel_step * sign(x) * max(1, abs(x)), possibly adjusted to fit into the bounds. For method='3-point' the sign of h is ignored. If None (default) then step is selected automatically.
Relative tolerance for x. Terminate successfully if step size is less than xk * xrtol where xk is the current parameter vector.
Minimization of scalar function of one or more variables using the BFGS algorithm.
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