parameters: (func, args=(), brack=None, tol=1.48e-8, full_output=0, maxiter=500)
docstring: Given a function of one-variable and a possible bracket, return
      the local 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.
  
      Does not ensure that the minimum lies in the range specified by
      `brack`. See `fminbound`.
  
      Examples
      --------
      We illustrate the behaviour of the function when `brack` is of
      size 2 and 3 respectively. In the case where `brack` is of the
      form (xa,xb), we can see for the given values, the output need
      not necessarily lie in the range (xa,xb).
  
      >>> def f(x):
      ...     return x**2
  
      >>> from scipy import optimize
  
      >>> minimum = optimize.brent(f,brack=(1,2))
      >>> minimum
      0.0
      >>> minimum = optimize.brent(f,brack=(-1,0.5,2))
      >>> minimum
      -2.7755575615628914e-17
docstring_summary: Given a function of one-variable and a possible bracket, return
      the local minimum of the function isolated to a fractional precision
      of tol.
docstring_tokens: Given a function of one - variable and a possible bracket return the local minimum of the function isolated to a fractional precision of tol .
function: def brent(func, args=(), brack=None, tol=1.48e-8, full_output=0, maxiter=500):
      """
      Given a function of one-variable and a possible bracket, return
      the local 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.
  
      Does not ensure that the minimum lies in the range specified by
      `brack`. See `fminbound`.
  
      Examples
      --------
      We illustrate the behaviour of the function when `brack` is of
      size 2 and 3 respectively. In the case where `brack` is of the
      form (xa,xb), we can see for the given values, the output need
      not necessarily lie in the range (xa,xb).
  
      >>> def f(x):
      ...     return x**2
  
      >>> from scipy import optimize
  
      >>> minimum = optimize.brent(f,brack=(1,2))
      >>> minimum
      0.0
      >>> minimum = optimize.brent(f,brack=(-1,0.5,2))
      >>> minimum
      -2.7755575615628914e-17
  
      """
      options = {'xtol': tol,
                 'maxiter': maxiter}
      res = _minimize_scalar_brent(func, brack, args, **options)
      if full_output:
          return res['x'], res['fun'], res['nit'], res['nfev']
      else:
          return res['x']
function_tokens: def brent ( func , args = ( ) , brack = None , tol = 1.48e-8 , full_output = 0 , maxiter = 500 ) : options = { 'xtol' : tol , 'maxiter' : maxiter } res = _minimize_scalar_brent ( func , brack , args , * * options ) if full_output : return res [ 'x' ] , res [ 'fun' ] , res [ 'nit' ] , res [ 'nfev' ] else : return res [ 'x' ]