diff --git a/test/test_optimizers/test_optimizers.py b/test/test_optimizers/test_optimizers.py
index 5e459c0e..a2518476 100644
--- a/test/test_optimizers/test_optimizers.py
+++ b/test/test_optimizers/test_optimizers.py
@@ -508,60 +508,38 @@ def test_stable_linear_sr3_linear_library():
     assert np.allclose(opt.coef_.flatten(), 0.0)
 
 
-@pytest.mark.parametrize(
-    "params",
-    [
-        dict(regularizer="l1", reg_weight_lam=0, _include_bias=True),
-        dict(regularizer="l1", reg_weight_lam=1e-5, _include_bias=True),
-        dict(
-            regularizer="weighted_l1",
-            reg_weight_lam=np.zeros((1, 2)),
-            eta=1e5,
-            alpha_m=1e4,
-            alpha_A=1e5,
-            _include_bias=False,
-        ),
-        dict(
-            regularizer="weighted_l1",
-            reg_weight_lam=1e-5 * np.ones((1, 2)),
-            _include_bias=False,
-        ),
-        dict(regularizer="l2", reg_weight_lam=0, _include_bias=True),
-        dict(regularizer="l2", reg_weight_lam=1e-5, _include_bias=True),
-        dict(
-            regularizer="weighted_l2",
-            reg_weight_lam=np.zeros((1, 2)),
-            _include_bias=False,
-        ),
-        dict(
-            regularizer="weighted_l2",
-            reg_weight_lam=1e-5 * np.ones((1, 2)),
-            _include_bias=False,
-        ),
-    ],
-)
-def test_trapping_sr3_quadratic_library(params):
+@pytest.mark.parametrize("bias", (True, False))
+@pytest.mark.parametrize("method", ("global", "local"))
+@pytest.mark.parametrize("reg_weight", (0.0, 1e-1))
+def test_trapping_sr3_quadratic_library(bias, method, reg_weight):
     t = np.arange(0, 1, 0.1)
     x = np.exp(-t).reshape((-1, 1))
     x_dot = -x
     features = np.hstack([x, x**2])
-    if params.get("_include_bias"):
+    if bias:
         features = np.hstack([np.ones_like(x), features])
 
-    opt = TrappingSR3(_n_tgts=1, **params)
+    params = {
+        "_n_tgts": 1,
+        "_include_bias": bias,
+        "method": method,
+        "reg_weight_lam": reg_weight,
+    }
+
+    opt = TrappingSR3(**params)
     opt.fit(features, x_dot)
     check_is_fitted(opt)
 
     # Rerun with identity constraints
     r = x.shape[1]
-    N = 2 + params.get("_include_bias", 0)
+    N = 2 + bias
     params["constraint_rhs"] = np.zeros(r * N)
     params["constraint_lhs"] = np.eye(r * N, r * N)
 
-    opt = TrappingSR3(_n_tgts=1, **params)
+    opt = TrappingSR3(**params)
     opt.fit(features, x_dot)
     check_is_fitted(opt)
-    # check is solve was infeasible first
+    # check if solve was infeasible first
     if not np.allclose(opt.m_history_[-1], opt.m_history_[0]):
         assert np.allclose((opt.coef_.flatten())[0], 0.0, atol=1e-5)