pyodide/packages/nlopt/test_nlopt.py

66 lines
1.7 KiB
Python

from pyodide_build.testing import run_in_pyodide
@run_in_pyodide(
packages=["nlopt"],
xfail_browsers={
"chrome": "nlopt set_min_objective triggers a fatal runtime error in chrome 89 see #1493",
},
)
def test_nlopt():
import nlopt
import numpy as np
# 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)
assert np.linalg.norm(xopt - np.array([2.746310775, 15.0])) < 1e-7