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# scipy.optimize.fminbound

`scipy.optimize.``fminbound`(func, x1, x2, args=(), xtol=1e-05, maxfun=500, full_output=0, disp=1)

  Bounded minimization for scalar functions.

  Parameters: | func : callable f(x,*args) Objective function to be minimized (must accept and return scalars). x1, x2 : float or array scalar The optimization bounds. args : tuple, optional Extra arguments passed to function. xtol : float, optional The convergence tolerance. maxfun : int, optional Maximum number of function evaluations allowed. full_output : bool, optional If True, return optional outputs. disp : int, optional If non-zero, print messages. 0 : no message printing. 1 : non-convergence notification messages only. 2 : print a message on convergence too. 3 : print iteration results.
  Returns: | xopt : ndarray Parameters (over given interval) which minimize the objective function. fval : number The function value at the minimum point. ierr : int An error flag (0 if converged, 1 if maximum number of function calls reached). numfunc : int The number of function calls made.

  See also

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

  Notes

  Finds a local minimizer of the scalar function func in the interval x1 < xopt < x2 using Brent’s method. (See `brent` for auto-bracketing).