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import numpy as np
import pytest
from ase.utils.abc import Optimizable
class BoothFunctionOptimizable(Optimizable):
"""Optimizable based on the “Booth” function.
https://en.wikipedia.org/wiki/Test_functions_for_optimization
"""
def __init__(self, x0):
self.xy = np.array(x0)
def get_x(self):
return self.xy.copy()
def set_x(self, x):
self.xy[:] = x
@staticmethod
def ab(x, y):
return x + 2 * y - 7, 2 * x + y - 5
def get_value(self):
a, b = self.ab(*self.xy)
return a * a + b * b
def get_gradient(self):
x, y = self.xy
a, b = self.ab(*self.xy)
# XXX negative gradient
return -np.array([2 * a + 4 * b, 4 * a + 2 * b])
def iterimages(self):
return iter([])
def ndofs(self):
return len(self.xy)
def gradient_norm(self, gradient):
return np.linalg.norm(gradient)
def test_booth():
from ase.optimize.bfgs import BFGS
x0 = [1.234, 2.345]
target = BoothFunctionOptimizable(x0)
eps = 1e-8
with BFGS(target) as opt:
opt.run(fmax=eps)
assert target.xy == pytest.approx([1, 3], abs=eps)
assert target.get_value() == pytest.approx(0, abs=eps**2)
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