rgence too.

    ``3`` : print iteration results.

Returns
-------
xopt : ndarray
    Parameters (over given interval) which minimize the
    objective function.
fval : number
    (Optional output) The function value evaluated at the minimizer.
ierr : int
    (Optional output) An error flag (0 if converged, 1 if maximum number of
    function calls reached).
numfunc : int
    (Optional output) 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.)

References
----------
.. [1] Forsythe, G.E., M. A. Malcolm, and C. B. Moler. "Computer Methods
       for Mathematical Computations." Prentice-Hall Series in Automatic
       Computation 259 (1977).
.. [2] Brent, Richard P. Algorithms for Minimization Without Derivatives.
       Courier Corporation, 2013.

Examples
--------
`fminbound` finds the minimizer of the function in the given range.
The following examples illustrate this.

>>> from scipy import optimize
>>> def f(x):
...     return (x-1)**2
>>> minimizer = optimize.fminbound(f, -4, 4)
>>> minimizer
1.0
>>> minimum = f(minimizer)
>>> minimum
0.0
>>> res = optimize.fminbound(f, 3, 4, full_output=True)
>>> minimizer, fval, ierr, numfunc = res
>>> minimizer
3.000005960860986
>>> minimum = f(minimizer)
>>> minimum, fval
(4.000023843479476, 4.000023843479476)
)