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# scipy.optimize.bracket

`scipy.optimize.``bracket`(func, xa=0.0, xb=1.0, args=(), grow_limit=110.0, maxiter=1000)

  Given a function and distinct initial points, search in the downhill direction (as defined by the initital points) and return new points xa, xb, xc that bracket the minimum of the function f(xa) > f(xb) < f(xc). It doesn’t always mean that obtained solution will satisfy xa<=x<=xb

  Parameters: | func : callable f(x,*args) Objective function to minimize. xa, xb : float Bracketing interval. args : tuple Additional arguments (if present), passed to func. grow_limit : float Maximum grow limit. maxiter : int Maximum number of iterations to perform.
  Returns: | xa, xb, xc, fa, fb, fc, funcalls xa, xb, xc : float Bracket. fa, fb, fc : float Objective function values in bracket. funcalls : int Number of function evaluations made.

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