_minimize_tnc(fun, x0, args=(), jac=None, bounds=None, eps=1e-08, scale=None, offset=None, mesg_num=None, maxCGit=-1, maxiter=None, eta=-1, stepmx=0, accuracy=0, minfev=0, ftol=-1, xtol=-1, gtol=-1, rescale=-1, disp=False, callback=None, finite_diff_rel_step=None, maxfun=None, **unknown_options)
If :None:None:`jac is None` the absolute step size used for numerical approximation of the jacobian via forward differences.
Scaling factors to apply to each variable. If None, the factors are up-low for interval bounded variables and 1+|x] fo the others. Defaults to None.
Value to subtract from each variable. If None, the offsets are (up+low)/2 for interval bounded variables and x for the others.
Set to True to print convergence messages.
Maximum number of hessian*vector evaluations per main iteration. If maxCGit == 0, the direction chosen is -gradient if maxCGit < 0, maxCGit is set to max(1,min(50,n/2)). Defaults to -1.
Maximum number of function evaluations. This keyword is deprecated in favor of maxfun
. Only if maxfun
is None is this keyword used.
Severity of the line search. If < 0 or > 1, set to 0.25. Defaults to -1.
Maximum step for the line search. May be increased during call. If too small, it will be set to 10.0. Defaults to 0.
Relative precision for finite difference calculations. If <= machine_precision, set to sqrt(machine_precision). Defaults to 0.
Minimum function value estimate. Defaults to 0.
Precision goal for the value of f in the stopping criterion. If ftol < 0.0, ftol is set to 0.0 defaults to -1.
Precision goal for the value of x in the stopping criterion (after applying x scaling factors). If xtol < 0.0, xtol is set to sqrt(machine_precision). Defaults to -1.
Precision goal for the value of the projected gradient in the stopping criterion (after applying x scaling factors). If gtol < 0.0, gtol is set to 1e-2 * sqrt(accuracy). Setting it to 0.0 is not recommended. Defaults to -1.
Scaling factor (in log10) used to trigger f value rescaling. If 0, rescale at each iteration. If a large value, never rescale. If < 0, rescale is set to 1.3.
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.
Maximum number of function evaluations. If None, maxfun
is set to max(100, 10*len(x0)). Defaults to None.
Minimize a scalar function of one or more variables using a truncated Newton (TNC) algorithm.
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