# scipy.optimize.brent¶

scipy.optimize.brent(func, args=(), brack=None, tol=1.48e-08, full_output=0, maxiter=500)[source]

Given a function of one-variable and a possible bracket, return the local minimum of the function isolated to a fractional precision of tol.

Parameters: func : callable f(x,*args) Objective function. args : tuple, optional Additional arguments (if present). brack : tuple, optional Either a triple (xa,xb,xc) where xa

minimize_scalar
Interface to minimization algorithms for scalar univariate functions. See the ‘Brent’ method in particular.

Notes

Uses inverse parabolic interpolation when possible to speed up convergence of golden section method.

Does not ensure that the minimum lies in the range specified by brack. See fminbound.

Examples

We illustrate the behaviour of the function when brack is of size 2 and 3 respectively. In the case where brack is of the form (xa,xb), we can see for the given values, the output need not necessarily lie in the range (xa,xb).

>>> def f(x):
...     return x**2

>>> from scipy import optimize

>>> minimum = optimize.brent(f,brack=(1,2))
>>> minimum
0.0
>>> minimum = optimize.brent(f,brack=(-1,0.5,2))
>>> minimum
-2.7755575615628914e-17


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