Fix bounds parsing in Scipy optimizers and warn when unsupported (qiskit-community#155)

Co-authored-by: Steve Wood <40241007+woodsp-ibm@users.noreply.github.com>

Author: 2 people

qiskit_algorithms/exceptions.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2017, 2023.
+# (C) Copyright IBM 2017, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -10,7 +10,7 @@
 # copyright notice, and modified files need to carry a notice indicating
 # that they have been altered from the originals.
 
-"""Exception for errors raised by Algorithms module."""
+"""Exception and warnings for errors raised by Algorithms module."""
 
 from qiskit.exceptions import QiskitError
 
@@ -19,3 +19,22 @@ class AlgorithmError(QiskitError):
     """For Algorithm specific errors."""
 
     pass
+
+
+class QiskitAlgorithmsWarning(UserWarning):
+    """Base class for warnings raised by Qiskit Algorithms."""
+
+    def __init__(self, *message):
+        """Set the error message."""
+        super().__init__(" ".join(message))
+        self.message = " ".join(message)
+
+    def __str__(self):
+        """Return the message."""
+        return repr(self.message)
+
+
+class QiskitAlgorithmsOptimizersWarning(QiskitAlgorithmsWarning):
+    """For Algorithm specific warnings."""
+
+    pass

qiskit_algorithms/optimizers/scipy_optimizer.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -76,6 +76,17 @@ def __init__(
         self._max_evals_grouped = max_evals_grouped
         self._kwargs = kwargs
 
+        if "bounds" in self._kwargs:
+            raise RuntimeError(
+                "Optimizer bounds should be passed to SciPyOptimizer.minimize() and is not  "
+                "supported in SciPyOptimizer constructor kwargs."
+            )
+        if "bounds" in self._options:
+            raise RuntimeError(
+                "Optimizer bounds should be passed to SciPyOptimizer.minimize() and not as "
+                "options."
+            )
+
     def get_support_level(self):
         """Return support level dictionary"""
         return {
@@ -116,9 +127,12 @@ def minimize(
         jac: Callable[[POINT], POINT] | None = None,
         bounds: list[tuple[float, float]] | None = None,
     ) -> OptimizerResult:
-        # Remove ignored parameters to suppress the warning of scipy.optimize.minimize
+
+        # Remove ignored bounds to suppress the warning of scipy.optimize.minimize
         if self.is_bounds_ignored:
             bounds = None
+
+        # Remove ignored gradient to suppress the warning of scipy.optimize.minimize
         if self.is_gradient_ignored:
             jac = None
 

releasenotes/notes/0.3/raise-error-incorrect-bounds-passing-c878f155221fa8f4.yaml

@@ -0,0 +1,6 @@
+---
+fixes:
+  - |
+    Resolved the issue with multiply-defined Scipy bounds in Optimizers. In line with Scipy, now only the ``minimize()``
+    method supports the ``bounds`` keyword. An error is raised when trying to pass ``bounds`` in the Optimizer constructor
+    via ``kwargs`` or ``options``.

test/optimizers/test_optimizers.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2018, 2023.
+# (C) Copyright IBM 2018, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -149,7 +149,7 @@ def test_gsls(self):
             max_eval=10000,
             min_step_size=1.0e-12,
         )
-        x_0 = [1.3, 0.7, 0.8, 1.9, 1.2]
+        x_0 = np.asarray([1.3, 0.7, 0.8, 1.9, 1.2])
 
         algorithm_globals.random_seed = 1
         res = optimizer.minimize(rosen, x_0)
@@ -183,6 +183,39 @@ def callback(x):
         self.run_optimizer(optimizer, max_nfev=10000)
         self.assertTrue(values)  # Check the list is nonempty.
 
+    def test_scipy_optimizer_parse_bounds(self):
+        """
+        Test the parsing of bounds in SciPyOptimizer.minimize method. Verifies that the bounds are
+        correctly parsed and set within the optimizer object.
+
+        Raises:
+            AssertionError: If any of the assertions fail.
+            AssertionError: If a TypeError is raised unexpectedly while parsing bounds.
+
+        """
+        try:
+            # Initialize SciPyOptimizer instance with SLSQP method
+            optimizer = SciPyOptimizer("SLSQP")
+
+            # Call minimize method with a simple lambda function and bounds
+            optimizer.minimize(lambda x: -x, 1.0, bounds=[(0.0, 1.0)])
+
+            # Assert that "bounds" is not present in optimizer options and kwargs
+            self.assertFalse("bounds" in optimizer._options)
+            self.assertFalse("bounds" in optimizer._kwargs)
+
+        except TypeError:
+            # This would give: https://github.com/qiskit-community/qiskit-machine-learning/issues/570
+            self.fail(
+                "TypeError was raised unexpectedly when parsing bounds in SciPyOptimizer.minimize(...)."
+            )
+
+        # Finally, expect exceptions if bounds are parsed incorrectly, i.e. differently than as in Scipy
+        with self.assertRaises(RuntimeError):
+            _ = SciPyOptimizer("SLSQP", bounds=[(0.0, 1.0)])
+        with self.assertRaises(RuntimeError):
+            _ = SciPyOptimizer("SLSQP", options={"bounds": [(0.0, 1.0)]})
+
     # ESCH and ISRES do not do well with rosen
     @data(
         (CRS, True),