Store cuts and lazy constraints as JSON in H5

miplearn/problems/stab.py

@@ -170,11 +170,11 @@ def _stab_read(data: Union[str, MaxWeightStableSetData]) -> MaxWeightStableSetDa
     return data
 
 
-def _stab_separate(data: MaxWeightStableSetData, x_val: List[float]) -> List[Hashable]:
+def _stab_separate(data: MaxWeightStableSetData, x_val: List[float]) -> List:
     # Check that we selected at most one vertex for each
     # clique in the graph (sum <= 1)
-    violations: List[Hashable] = []
+    violations: List[Any] = []
     for clique in nx.find_cliques(data.graph):
         if sum(x_val[i] for i in clique) > 1.0001:
-            violations.append(tuple(sorted(clique)))
+            violations.append(sorted(clique))
     return violations

miplearn/problems/tsp.py

@@ -231,18 +231,18 @@ def _tsp_separate(
     x_val: dict[Tuple[int, int], float],
     edges: List[Tuple[int, int]],
     n_cities: int,
-) -> List[Tuple[Tuple[int, int], ...]]:
+) -> List:
     violations = []
     selected_edges = [e for e in edges if x_val[e] > 0.5]
     graph = nx.Graph()
     graph.add_edges_from(selected_edges)
     for component in list(nx.connected_components(graph)):
         if len(component) < n_cities:
-            cut_edges = tuple(
-                (e[0], e[1])
+            cut_edges = [
+                [e[0], e[1]]
                 for e in edges
                 if (e[0] in component and e[1] not in component)
                 or (e[0] not in component and e[1] in component)
-            )
+            ]
             violations.append(cut_edges)
     return violations