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# scipy.optimize.brute

`scipy.optimize.``brute`(func, ranges, args=(), Ns=20, full_output=0, finish=<function fmin at 0x55f20c8>)

  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.
  Returns: | (x0, fval, {grid, Jout}) 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: | Find the minimum of a function evaluated on a grid given by the tuple ranges.

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