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.

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.

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.


>>> @njit
... def f(x):
...     return -(x + 2.0)**2 + 1.0
>>> xf, fval, info = brent_max(f, -2, 2)