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# scipy.optimize.fmin_tnc

`scipy.optimize.``fmin_tnc`(func, x0, fprime=None, args=(), approx_grad=0, bounds=None, epsilon=1e-08, scale=None, offset=None, messages=15, maxCGit=-1, maxfun=None, eta=-1, stepmx=0, accuracy=0, fmin=0, ftol=-1, xtol=-1, pgtol=-1, rescale=-1)

  Minimize a function with variables subject to bounds, using gradient information.

  Parameters: | func : callable func(x, *args) Function to minimize. Should return f and g, where f is the value of the function and g its gradient (a list of floats). If the function returns None, the minimization is aborted. x0 : list of floats Initial estimate of minimum. fprime : callable fprime(x, *args) Gradient of func. If None, then func must return the function value and the gradient (f,g = func(x, *args)). args : tuple Arguments to pass to function. approx_grad : bool If true, approximate the gradient numerically. bounds : list (min, max) pairs for each element in x, defining the bounds on that parameter. Use None or +/-inf for one of min or max when there is no bound in that direction. 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] fo the others. Defaults to None offset : float Value to substract from each variable. If None, the offsets are (up+low)/2 for interval bounded variables and x for the others. messages : Bit mask used to select messages display during minimization values defined in the MSGS dict. Defaults to MGS_ALL. 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. maxfun : int Maximum number of function evaluation. if None, maxfun is set to max(100, 10*len(x0)). Defaults to None. 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. fmin : float Minimum function value estimate. Defaults to 0. ftol : float Precision goal for the value of f in the stoping 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. pgtol : float Precision goal for the value of the projected gradient in the stopping criterion (after applying x scaling factors). If pgtol < 0.0, pgtol 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.
  Returns: | x : list of floats The solution. nfeval : int The number of function evaluations. rc : Return code as defined in the RCSTRINGS dict.
  Seealso: | scikits.openopt, which offers a unified syntax to call this and other solvers fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg : multivariate local optimizers leastsq : nonlinear least squares minimizer fmin_l_bfgs_b, fmin_tnc, fmin_cobyla : constrained multivariate optimizers anneal, brute : global optimizers fminbound, brent, golden, bracket : local scalar minimizers fsolve : n-dimenstional root-finding brentq, brenth, ridder, bisect, newton : one-dimensional root-finding fixed_point : scalar fixed-point finder

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