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# scipy.optimize.fminbound

`scipy.optimize.``fminbound`(func, x1, x2, args=(), xtol=1.0000000000000001e-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 Extra arguments passed to function. xtol : float The convergence tolerance. maxfun : int Maximum number of function evaluations allowed. full_output : bool If True, return optional outputs. disp : int 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, {fval, ierr, numfunc}) 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.
  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).

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