diff --git a/pypfopt/cla.py b/pypfopt/cla.py
index bde53b19..aec3b574 100644
--- a/pypfopt/cla.py
+++ b/pypfopt/cla.py
@@ -363,9 +363,10 @@ def _solve(self):
                         meanF.shape[0] - 1,
                         self.w[-1][i],
                     )
+                    lam_cmp = CLA._infnone(lam)
                     if (
-                        self.ls[-1] is None or lam < self.ls[-1]
-                    ) and lam > CLA._infnone(l_out):
+                        self.ls[-1] is None or lam_cmp < self.ls[-1]
+                    ) and lam_cmp > CLA._infnone(l_out):
                         l_out, i_out = lam, i
             if (l_in is None or l_in < 0) and (l_out is None or l_out < 0):
                 # 3) compute minimum variance solution
diff --git a/tests/test_cla.py b/tests/test_cla.py
index c5f69e23..72af8c6b 100644
--- a/tests/test_cla.py
+++ b/tests/test_cla.py
@@ -52,6 +52,20 @@ def test_cla_max_sharpe_short():
     assert sharpe > long_only_sharpe
 
 
+def test_cla_max_sharpe_equal_expected_returns_falls_back_to_min_var():
+    mu = np.array([0.1, 0.1])
+    cov = np.array([[0.01, 0.01], [0.01, 0.02]])
+
+    cla = CLA(mu, cov)
+    cla_min_var = CLA(mu, cov)
+    min_var_weights = cla_min_var.min_volatility()
+    w = cla.max_sharpe()
+
+    assert isinstance(w, dict)
+    np.testing.assert_allclose(cla.weights, cla_min_var.weights)
+    np.testing.assert_allclose(np.array(list(w.values())), np.array(list(min_var_weights.values())))
+
+
 def test_cla_custom_bounds():
     bounds = [(0.01, 0.13), (0.02, 0.11)] * 10
     cla = setup_cla(weight_bounds=bounds)