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# scipy.optimize.brute

`scipy.optimize.``brute`(func, ranges, args=(), Ns=20, full_output=0, finish=<function fmin at 0x483ade8>, disp=False)

  Minimize a function over a given range by brute force.

  Parameters : | func : callable `f(x,*args)` Objective function to be minimized. ranges : tuple Each element is a tuple of parameters or a slice object to be handed to `numpy.mgrid`. args : tuple Extra arguments passed to function. Ns : int Default number of samples, if those are not provided. full_output : bool If True, return the evaluation grid. finish : callable, optional An optimization function that is called with the result of brute force minimization as initial guess. finish should take the initial guess as positional argument, and take take args, full_output and disp as keyword arguments. See Notes for more details. disp : bool, optional Set to True to print convergence messages.
  Returns : | x0 : ndarray Value of arguments to func, giving minimum over the grid. fval : int Function value at minimum. grid : tuple Representation of the evaluation grid. It has the same length as x0. Jout : ndarray Function values over grid: `Jout = func(*grid)`.

  Notes

  The range is respected by the brute force minimization, but if the finish keyword specifies another optimization function (including the default `fmin`), the returned value may still be (just) outside the range. In order to ensure the range is specified, use `finish=None`.

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