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bundles / scipy latest / scipy / optimize / _tnc / _minimize_tnc

function

scipy.optimize._tnc:_minimize_tnc

source: /scipy/optimize/_tnc.py :285

Signature

def   _minimize_tnc ( fun ,  x0 ,  args = () ,  jac = None ,  bounds = None ,  eps = 1e-08 ,  scale = None ,  offset = None ,  mesg_num = None ,  maxCGit = -1 ,  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 ,  workers = None ,  ** unknown_options )

Summary

Minimize a scalar function of one or more variables using a truncated Newton (TNC) algorithm.

Parameters

eps : float or ndarray

If jac is None the absolute step size used for numerical approximation of the jacobian via forward differences.

scale : list of floats

Scaling factors to apply to each variable. If None, the factors are up-low for interval bounded variables and 1+|x] for the others. Defaults to None.

offset : float

Value to subtract from each variable. If None, the offsets are (up+low)/2 for interval bounded variables and x for the others.

disp : bool

Set to True to print convergence messages.

maxCGit : int

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.

eta : float

Severity of the line search. If < 0 or > 1, set to 0.25. Defaults to -1.

stepmx : float

Maximum step for the line search. May be increased during call. If too small, it will be set to 10.0. Defaults to 0.

accuracy : float

Relative precision for finite difference calculations. If <= machine_precision, set to sqrt(machine_precision). Defaults to 0.

minfev : float

Minimum function value estimate. Defaults to 0.

ftol : float

Precision goal for the value of f in the stopping criterion. If ftol < 0.0, ftol is set to 0.0 defaults to -1.

xtol : float

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.

gtol : float

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.

rescale : float

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.

finite_diff_rel_step : None or array_like, optional

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.

maxfun : int

Maximum number of function evaluations. If None, maxfun is set to max(100, 10*len(x0)). Defaults to None.

workers : int, map-like callable, optional

A map-like callable, such as multiprocessing.Pool.map for evaluating any numerical differentiation in parallel. This evaluation is carried out as workers(fun, iterable).

Aliases

  • scipy.optimize._minimize._minimize_tnc