def test_nlopt(selenium):
    selenium.load_package("nlopt")
    assert selenium.run(
        """
import numpy as np
import nlopt

# objective function
def f(x, grad):
    x0 = x[0]
    x1 = x[1]
    y = 67.8306620138889-13.5689721666667*x0-3.83269458333333*x1+\
        0.720841066666667*x0**2+0.3427605*x0*x1+\
        0.0640322916666664*x1**2

    grad[0] = 1.44168213333333*x0 + 0.3427605*x1 - 13.5689721666667
    grad[1] = 0.3427605*x0 + 0.128064583333333*x1 - 3.83269458333333

    return y

# inequality constraint (constrained to be <= 0)
def h(x, grad):
    x0 = x[0]
    x1 = x[1]
    z = -3.72589930555515+128.965158333333*x0+0.341479166666643*x1-\
        0.19642666666667*x0**2+2.78692500000002*x0*x1-\
        0.0000104166666686543*x1**2-468.897287036862

    grad[0] = -0.39285333333334*x0 + 2.78692500000002*x1 + 128.965158333333
    grad[1] = 2.78692500000002*x0 - 2.08333333373086e-5*x1 + 0.341479166666643

    return z

opt = nlopt.opt(nlopt.LD_SLSQP, 2)
opt.set_min_objective(f)

opt.set_lower_bounds(np.array([2.5, 7]))
opt.set_upper_bounds(np.array([7.5, 15]))

opt.add_inequality_constraint(h)

opt.set_ftol_rel(1.0e-6)

x0 = np.array([5, 11])

xopt = opt.optimize(x0)

np.linalg.norm(xopt - np.array([2.746310775, 15.0])) < 1e-7
"""
    )