def test_SLSQP_with_inequality_constraints(self, rosenbrock_function):
# Given
cost_function = FunctionWithGradient(
rosenbrock_function,
finite_differences_gradient(rosenbrock_function))
constraints = {"type": "ineq", "fun": lambda x: x[0] + x[1] - 3}
optimizer = ScipyOptimizer(method="SLSQP")
initial_params = np.array([0, 0])
# When
results_without_constraints = optimizer.minimize(
cost_function, initial_params=initial_params)
optimizer.constraints = constraints
results_with_constraints = optimizer.minimize(
cost_function, initial_params=initial_params)
# Then
assert results_without_constraints.opt_value == pytest.approx(
results_with_constraints.opt_value, abs=1e-1)
assert results_with_constraints.opt_params.sum() >= 3
def test_SLSQP_with_equality_constraints(self, sum_x_squared,
rosenbrock_function):
# Given
cost_function = FunctionWithGradient(
rosenbrock_function,
finite_differences_gradient(rosenbrock_function))
constraint_cost_function = sum_x_squared
constraints = ({"type": "eq", "fun": constraint_cost_function}, )
optimizer = ScipyOptimizer(method="SLSQP", constraints=constraints)
initial_params = np.array([1, 1])
target_params = np.array([0, 0])
target_value = 1
# When
results = optimizer.minimize(cost_function,
initial_params=initial_params)
# Then
assert results.opt_value == pytest.approx(target_value, abs=1e-3)
assert results.opt_params == pytest.approx(target_params, abs=1e-3)