Make MinimizeMaximize inherit from Canonical

dsp/problem.py

@@ -5,7 +5,6 @@
 
 import cvxpy as cp
 import numpy as np
-from cvxpy.atoms.atom import Atom
 from cvxpy.constraints.constraint import Constraint
 from cvxpy.problems.objective import Objective
 from cvxpy.utilities import Canonical
@@ -20,10 +19,10 @@
 from dsp.saddle_extremum import SaddleExtremum
 
 
-class MinimizeMaximize:
+class MinimizeMaximize(Canonical):
     def __init__(self, expr: cp.Expression) -> None:
         self._validate_arguments(expr)
-        self.expr = Atom.cast_to_const(expr)
+        self.args = [cp.Expression.cast_to_const(expr)]
 
     @staticmethod
     def _validate_arguments(expr: cp.Expression | float | int) -> None:
@@ -45,7 +44,7 @@ def value(self) -> float:
 class SaddlePointProblem(cp.Problem):
     def __init__(
         self,
-        minmax_objective: MinimizeMaximize | Objective,
+        minmax_objective: MinimizeMaximize,
         constraints: list[Constraint] | None = None,
         minimization_vars: Iterable[cp.Variable] | None = None,
         maximization_vars: Iterable[cp.Variable] | None = None,

tests/test_minimizemaximize.py

@@ -1,6 +1,7 @@
 import cvxpy as cp
 import pytest
 
+import dsp
 from dsp.problem import MinimizeMaximize
 
 
@@ -17,3 +18,16 @@ def test_value():
 def test_invalid_argument():
     with pytest.raises(TypeError):
         MinimizeMaximize("hello")
+
+
+def test_properties():
+    x = cp.Variable()
+    y = cp.Variable()
+    f = dsp.saddle_inner(x, y)
+
+    obj = MinimizeMaximize(f)
+    assert obj.is_dsp()
+    assert obj.value is None
+    assert obj.variables() == [x, y]
+    assert obj.parameters() == []
+    assert obj.atoms() == [dsp.saddle_inner]