#### Previous topic

scipy.optimize.fminbound

#### Next topic

scipy.optimize.golden

# scipy.optimize.brent

`scipy.optimize.``brent`(func, args=(), brack=None, tol=1.48e-08, full_output=0, maxiter=500)

  Given a function of one-variable and a possible bracketing interval, return the minimum of the function isolated to a fractional precision of tol.

  Parameters: | func : callable f(x,*args) Objective function. args : tuple, optional Additional arguments (if present). brack : tuple, optional Either a triple (xa,xb,xc) where xa<xb<xc and func(xb) < func(xa), func(xc) or a pair (xa,xb) which are used as a starting interval for a downhill bracket search (see `bracket`). Providing the pair (xa,xb) does not always mean the obtained solution will satisfy xa<=x<=xb. tol : float, optional Stop if between iteration change is less than tol. full_output : bool, optional If True, return all output args (xmin, fval, iter, funcalls). maxiter : int, optional Maximum number of iterations in solution.
  Returns: | xmin : ndarray Optimum point. fval : float Optimum value. iter : int Number of iterations. funcalls : int Number of objective function evaluations made.

  See also

  `minimize_scalar`
    Interface to minimization algorithms for scalar univariate functions. See the ‘Brent’ method in particular.

  Notes

  Uses inverse parabolic interpolation when possible to speed up convergence of golden section method.