From 19bd785ae430d5e81aaf1cea3b52812edc21cc56 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ga=C3=A9tan=20Lepage?=
 <33058747+GaetanLepage@users.noreply.github.com>
Date: Sun, 21 Jul 2024 23:54:24 +0200
Subject: [PATCH] Fix scipy 1.14 incompatibilities (#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   | 4 ++--
 cvxpy/atoms/gen_lambda_max.py     | 2 +-
 cvxpy/atoms/lambda_max.py         | 2 +-
 cvxpy/tests/test_conic_solvers.py | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/cvxpy/atoms/condition_number.py b/cvxpy/atoms/condition_number.py
index 9bb598249b..e30e0d515e 100644
--- a/cvxpy/atoms/condition_number.py
+++ b/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]:
diff --git a/cvxpy/atoms/gen_lambda_max.py b/cvxpy/atoms/gen_lambda_max.py
index 9316a46c60..eff00748bb 100644
--- a/cvxpy/atoms/gen_lambda_max.py
+++ b/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.
diff --git a/cvxpy/atoms/lambda_max.py b/cvxpy/atoms/lambda_max.py
index 58da2185d3..b42f9a2fec 100644
--- a/cvxpy/atoms/lambda_max.py
+++ b/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.
diff --git a/cvxpy/tests/test_conic_solvers.py b/cvxpy/tests/test_conic_solvers.py
index 6a0b95478f..e2efa7baf7 100644
--- a/cvxpy/tests/test_conic_solvers.py
+++ b/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