scalar_maximization

quantecon.optimize.scalar_maximization.brent_max(func, a, b, args=(), xtol=1e-05, 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:

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)