_minimize_cg(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, **unknown_options)
Set to True to print convergence messages.
Maximum number of iterations to perform.
Gradient norm must be less than :None:None:`gtol` before successful termination.
Order of norm (Inf is max, -Inf is min).
If :None:None:`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 :None:None:`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(x0) * max(1, abs(x0))
, possibly adjusted to fit into the bounds. For method='3-point'
the sign of :None:None:`h` is ignored. If None (default) then step is selected automatically.
Minimization of scalar function of one or more variables using the conjugate gradient algorithm.
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