Uses a jitted version of the maximization routine from SciPy's fminbound.
    The algorithm is identical except that it's been switched to maximization
    rather than minimization, and the tests for convergence have been stripped
    out to allow for jit compilation.

    Note that the input function `func` must be jitted or the call will fail.

    Parameters
    ----------
    func : jitted function
    a : scalar
        Lower bound for search
    b : scalar
        Upper bound for search
    args : tuple, optional
        Extra arguments passed to the objective function.
    maxiter : int, optional
        Maximum number of iterations to perform.
    xtol : float, optional
        Absolute error in solution `xopt` acceptable for convergence.

    Returns
    -------
    xf : float
        The maximizer
    fval : float
        The maximum value attained
    info : tuple
        A tuple of the form (status_flag, num_iter).  Here status_flag
        indicates whether or not the maximum number of function calls was
        attained.  A value of 0 implies that the maximum was not hit.
        The value `num_iter` is the number of function calls.

    Example
    -------

    ```
        @njit
        def f(x):
            return -(x + 2.0)**2 + 1.0

        xf, fval, info = brent_max(f, -2, 2)
    ```

def brent_max(func, a, b, args=(), xtol=1e-5, maxiter=500):
    """
    Uses a jitted version of the maximization routine from SciPy's fminbound.
    The algorithm is identical except that it's been switched to maximization
    rather than minimization, and the tests for convergence have been stripped
    out to allow for jit compilation.

    Note that the input function `func` must be jitted or the call will fail.

    Parameters
    ----------
    func : jitted function
    a : scalar
        Lower bound for search
    b : scalar
        Upper bound for search
    args : tuple, optional
        Extra arguments passed to the objective function.
    maxiter : int, optional
        Maximum number of iterations to perform.
    xtol : float, optional
        Absolute error in solution `xopt` acceptable for convergence.

    Returns
    -------
    xf : float
        The maximizer
    fval : float
        The maximum value attained
    info : tuple
        A tuple of the form (status_flag, num_iter).  Here status_flag
        indicates whether or not the maximum number of function calls was
        attained.  A value of 0 implies that the maximum was not hit.
        The value `num_iter` is the number of function calls.

    Example
    -------

    ```
        @njit
        def f(x):
            return -(x + 2.0)**2 + 1.0

        xf, fval, info = brent_max(f, -2, 2)
    ```

    """
    if not np.isfinite(a):
        raise ValueError("a must be finite.")

    if not np.isfinite(b):
        raise ValueError("b must be finite.")

    if not a < b:
        raise ValueError("a must be less than b.")

    maxfun = maxiter
    status_flag = 0

    sqrt_eps = np.sqrt(2.2e-16)
    golden_mean = 0.5 * (3.0 - np.sqrt(5.0))

    fulc = a + golden_mean * (b - a)
    nfc, xf = fulc, fulc
    rat = e = 0.0
    x = xf
    fx = -func(x, *args)
    num = 1

    ffulc = fnfc = fx
    xm = 0.5 * (a + b)
    tol1 = sqrt_eps * np.abs(xf) + xtol / 3.0
    tol2 = 2.0 * tol1

    while (np.abs(xf - xm) > (tol2 - 0.5 * (b - a))):
        golden = 1
        # Check for parabolic fit
        if np.abs(e) > tol1:
            golden = 0
            r = (xf - nfc) * (fx - ffulc)
            q = (xf - fulc) * (fx - fnfc)
            p = (xf - fulc) * q - (xf - nfc) * r
            q = 2.0 * (q - r)
            if q > 0.0:
                p = -p
            q = np.abs(q)
            r = e
            e = rat

            # Check for acceptability of parabola
            if ((np.abs(p) < np.abs(0.5*q*r)) and (p > q*(a - xf)) and
                    (p < q * (b - xf))):
                rat = (p + 0.0) / q
                x = xf + rat

                if ((x - a) < tol2) or ((b - x) < tol2):
                    si = np.sign(xm - xf) + ((xm - xf) == 0)
                    rat = tol1 * si
            else:      # do a golden section step
                golden = 1

        if golden:  # Do a golden-section step
            if xf >= xm:
                e = a - xf
            else:
                e = b - xf
            rat = golden_mean*e

        if rat == 0:
            si = np.sign(rat) + 1
        else:
            si = np.sign(rat)

        x = xf + si * np.maximum(np.abs(rat), tol1)
        fu = -func(x, *args)
        num += 1

        if fu <= fx:
            if x >= xf:
                a = xf
            else:
                b = xf
            fulc, ffulc = nfc, fnfc
            nfc, fnfc = xf, fx
            xf, fx = x, fu
        else:
            if x < xf:
                a = x
            else:
                b = x
            if (fu <= fnfc) or (nfc == xf):
                fulc, ffulc = nfc, fnfc
                nfc, fnfc = x, fu
            elif (fu <= ffulc) or (fulc == xf) or (fulc == nfc):
                fulc, ffulc = x, fu

        xm = 0.5 * (a + b)
        tol1 = sqrt_eps * np.abs(xf) + xtol / 3.0
        tol2 = 2.0 * tol1

        if num >= maxfun:
            status_flag = 1
            break

    fval = -fx
    info = status_flag, num

    return xf, fval, info