speed op rosenborck test

tests/algorithms/test_optimizers.py

@@ -9,16 +9,17 @@
 
 @pytest.mark.parametrize('enforce_bounds_on_x1', [True, False])
 @pytest.mark.parametrize(
-    ('optimizer', 'optimizer_kwargs'), [(adam, {'lr': 0.02, 'max_iter': 10000}), (lbfgs, {'lr': 1.0})]
+    ('optimizer', 'optimizer_kwargs'),
+    [(adam, {'lr': 0.02, 'max_iter': 2000, 'betas': (0.8, 0.999)}), (lbfgs, {'lr': 1.0, 'max_iter': 20})],
 )
 def test_optimizers_rosenbrock(optimizer, enforce_bounds_on_x1, optimizer_kwargs):
     # use Rosenbrock function as test case with 2D test data
     a, b = 1.0, 100.0
     rosen_brock = Rosenbrock(a, b)
 
     # initial point of optimization
-    x1 = torch.tensor([a / 3.14])
-    x2 = torch.tensor([3.14])
+    x1 = torch.tensor([a / 1.23])
+    x2 = torch.tensor([1.23])
     x1.grad = torch.tensor([2.78])
     x2.grad = torch.tensor([-1.0])
     params_init = [x1, x2]
@@ -45,7 +46,7 @@ def test_optimizers_rosenbrock(optimizer, enforce_bounds_on_x1, optimizer_kwargs
         params_result = constrain_op(*params_result)
 
     # obtained solution should match analytical
-    torch.testing.assert_close(torch.tensor(params_result), analytical_solution)
+    torch.testing.assert_close(torch.tensor(params_result), analytical_solution, rtol=1e-4, atol=0)
 
     for p, before, grad_before in zip(params_init, params_init_before, params_init_grad_before, strict=True):
         assert p == before, 'the initial parameter should not have changed during optimization'