def test_cobyla_optimizer_with_quadratic_constraint(self):
""" Cobyla Optimizer Test With Quadratic Constraints. """
# load optimization problem
problem = QuadraticProgram()
problem.continuous_var(upperbound=1)
problem.continuous_var(upperbound=1)
problem.minimize(linear=[1, 1])
linear = [-1, -1]
quadratic = [[1, 0], [0, 1]]
problem.quadratic_constraint(linear=linear, quadratic=quadratic, rhs=-1/2)
# solve problem with cobyla
cobyla = CobylaOptimizer()
result = cobyla.solve(problem)
# analyze results
self.assertAlmostEqual(result.fval, 1.0, places=2)
def test_slsqp_optimizer_with_quadratic_constraint(self):
"""A test with equality constraint"""
problem = QuadraticProgram()
problem.continuous_var(upperbound=1)
problem.continuous_var(upperbound=1)
problem.minimize(linear=[1, 1])
linear = [-1, -1]
quadratic = [[1, 0], [0, 1]]
problem.quadratic_constraint(linear=linear, quadratic=quadratic, rhs=-1/2)
slsqp = SlsqpOptimizer()
solution = slsqp.solve(problem)
self.assertIsNotNone(solution)
self.assertIsNotNone(solution.x)
np.testing.assert_almost_equal([0.5, 0.5], solution.x, 3)
self.assertIsNotNone(solution.fval)
np.testing.assert_almost_equal(1., solution.fval, 3)
