added nonregression test

tests/scipy_wrappers_test.py

@@ -130,6 +130,18 @@ def test_maxiter_api(self):
     with self.assertRaises(ValueError):
       ScipyMinimize(fun=self.logreg_fun, maxiter=500, options={'maxiter': 100})
 
+  @parameterized.product(method=[
+    'Nelder-Mead',
+    'Powell',
+    'TNC',
+  ])
+  def test_no_njev(self, method):
+    # test methods that do not return njev
+    # see https://github.com/google/jaxopt/pull/542
+    solver = ScipyMinimize(fun=self.logreg_fun, method=method)
+    pytree_init = jnp.zeros([self.n_features, self.n_classes])
+    solver.run(pytree_init, l2reg=self.default_l2reg, data=self.data)
+
   def test_callback(self):
     # test the callback API
     trace = []
@@ -388,7 +400,7 @@ def wrapper(b):
       jac_idf = [list(blk_row) for blk_row in jac_idf]
     jac_idf = jnp.block(jac_idf)
     self.assertArraysAllClose(jac_theo, jac_idf, atol=1e-3)
-    
+
   def test_broyden(self):
     # non regression test based on issue https://github.com/google/jaxopt/issues/290
     def simple_root(x):
@@ -618,7 +630,7 @@ def setUp(self):
 
   def test_inverse_hessian_bfgs(self):
     # perform the bfgs algorithm search
-    res_scipy = osp.optimize.minimize(self.objective, self.x0, 
+    res_scipy = osp.optimize.minimize(self.objective, self.x0,
                                       method='BFGS', jac=self.derivative)
     # run the same algorithm via jaxopt
     res_jaxopt = ScipyMinimize(method='BFGS', fun=self.objective).run(self.x0)
@@ -627,7 +639,7 @@ def test_inverse_hessian_bfgs(self):
 
   def test_inverse_hessian_lbfgs(self):
     # perform the l-bfgs-b algorithm search
-    res_scipy = osp.optimize.minimize(self.objective, self.x0, 
+    res_scipy = osp.optimize.minimize(self.objective, self.x0,
                                       method='L-BFGS-B', jac=self.derivative)
     # run the same algorithm via jaxopt
     res_jaxopt = ScipyMinimize(method='L-BFGS-B', fun=self.objective).run(self.x0)