Fix scipy 1.14 incompatibilities (cvxpy#2508)

- The eigvals named argument of the scipy.linalg.eigvalsh has been
removed in favor of subset_by_index.
- The .A attribute of sparse matrices has been removed in favor of
.torray()

cvxpy/atoms/condition_number.py

@@ -34,8 +34,8 @@ def numeric(self, values):
         Requires that A be a Positive Semidefinite Matrix.
         """
         lo = hi = self.args[0].shape[0]-1
-        max_eigen = LA.eigvalsh(values[0], eigvals=(lo, hi))[0]
-        min_eigen = -LA.eigvalsh(-values[0], eigvals=(lo, hi))[0]
+        max_eigen = LA.eigvalsh(values[0], subset_by_index=(lo, hi))[0]
+        min_eigen = -LA.eigvalsh(-values[0], subset_by_index=(lo, hi))[0]
         return max_eigen / min_eigen
 
     def _domain(self) -> List[Constraint]:

cvxpy/atoms/gen_lambda_max.py

@@ -37,7 +37,7 @@ def numeric(self, values):
         return LA.eigh(a=values[0],
                        b=values[1],
                        eigvals_only=True,
-                       eigvals=(lo, hi))[0]
+                       subset_by_index=(lo, hi))[0]
 
     def _domain(self) -> List[Constraint]:
         """Returns constraints describing the domain of the node.

cvxpy/atoms/lambda_max.py

@@ -36,7 +36,7 @@ def numeric(self, values):
         Requires that A be symmetric.
         """
         lo = hi = self.args[0].shape[0]-1
-        return LA.eigvalsh(values[0], eigvals=(lo, hi))[0]
+        return LA.eigvalsh(values[0], subset_by_index=(lo, hi))[0]
 
     def _domain(self) -> List[Constraint]:
         """Returns constraints describing the domain of the node.

cvxpy/tests/test_conic_solvers.py

@@ -338,7 +338,7 @@ def test_quad_obj(self) -> None:
             constr = [x >= 1]
             prob = cp.Problem(cp.Minimize(expr), constr)
             data = prob.get_problem_data(solver=cp.SCS)
-            self.assertItemsAlmostEqual(data[0]["P"].A, 2*np.eye(2))
+            self.assertItemsAlmostEqual(data[0]["P"].toarray(), 2*np.eye(2))
             solution1 = prob.solve(solver=cp.SCS)
 
             # When use_quad_obj = False, the quadratic objective is