scalar_maximization

quantecon.optimize.scalar_maximization.brent_max(func, a, b, args=(), xtol=1e-05, maxiter=500)[source]

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:

funcjitted function
ascalar: Lower bound for search
bscalar: Upper bound for search
argstuple, optional: Extra arguments passed to the objective function.
maxiterint, optional: Maximum number of iterations to perform.
xtolfloat, optional: Absolute error in solution xopt acceptable for convergence.

Returns:

xffloat: The maximizer
fvalfloat: The maximum value attained
infotuple: 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.

Examples

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