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# minimize(method=’TNC’)

scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)

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

  See also

  For documentation for the rest of the parameters, see `scipy.optimize.minimize`

  Options:
    ——-
    epsfloat or ndarray

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

    scalelist 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.

    offsetfloat

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

    dispbool

      Set to True to print convergence messages.

    maxCGitint

      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.

    etafloat

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

    stepmxfloat

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

    accuracyfloat

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

    minfevfloat

      Minimum function value estimate. Defaults to 0.

    ftolfloat

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

    xtolfloat

      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.

    gtolfloat

      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.

    rescalefloat

      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_stepNone 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.

    maxfunint

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

    workersint, 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)`.

      Added in version 1.16.0.