experiments/tests/test_minimisation.py

from dolfinx_access import eval_Function_f64, min_Function_f64_p2


# A simple test that the finite-dimensional subproblem is solved correctly
def test_minimisation(pde):
    test = fem.Function(pde.V)

    # Beale function
    def beale(x):
        return (
            (1.5 - x[0] + x[0] * x[1]) ** 2
            + (2.25 - x[0] + x[0] * (x[1] ** 2)) ** 2
            + (2.625 - x[0] + x[0] * (x[1] ** 3)) ** 2
        )

    test.interpolate(beale)
    m = min_Function_f64_p2(test._cpp_object)
    print("Beale minimizer:", m.x, m.v)

    def quadratic_testf(x):
        return x[0] ** 2 - 5 * x[1] ** 2 + x[0] * x[1] - x[0] + x[1]

    testf = quadratic_testf
    test.interpolate(testf)
    n = 256
    dist = 0.0
    for x in np.linspace(pde.domain_size[0], pde.domain_size[1], n):
        for y in np.linspace(pde.domain_size[2], pde.domain_size[3], n):
            p = [x, y, 0.0]
            v = eval_Function_f64(test._cpp_object, p)
            v0 = testf(p)
            d = np.linalg.norm(v - v0)
            dist = max(d, dist)
    print("Quadratic function evaluation max discrepancy:", dist)