Add method argument to SimulatedAnnealing.autofit

alns/accept/SimulatedAnnealing.py

@@ -102,7 +102,12 @@ def __call__(self, rnd, best, current, candidate):
 
     @classmethod
     def autofit(
-        cls, init_obj: float, worse: float, accept_prob: float, num_iters: int
+        cls,
+        init_obj: float,
+        worse: float,
+        accept_prob: float,
+        num_iters: int,
+        method: str = "exponential",
     ) -> "SimulatedAnnealing":
         """
         Returns an SA object with initial temperature such that there is a
@@ -126,12 +131,16 @@ def autofit(
             worse than the initial solution.
         num_iters
             Number of iterations the ALNS algorithm will run.
+        method
+            The updating method, one of {'linear', 'exponential'}. Default
+            'exponential'.
 
         Raises
         ------
         ValueError
-            When ``worse`` not in [0, 1] or when ``accept_prob`` is not in
-            (0, 1).
+            When ``worse`` not in [0, 1], ``accept_prob`` is not in (0, 1),
+            ``num_iters`` is not positive, or when ``method`` is not one of
+            {'linear', 'exponential'}.
 
         Returns
         -------
@@ -154,12 +163,16 @@ def autofit(
         if not (0 < accept_prob < 1):
             raise ValueError("accept_prob outside (0, 1) not understood.")
 
-        if num_iters < 0:
-            raise ValueError("Negative number of iterations not understood.")
+        if num_iters <= 0:
+            raise ValueError("number of iterations <= 0 not understood.")
 
         start_temp = -worse * init_obj / np.log(accept_prob)
-        step = (1 / start_temp) ** (1 / num_iters)
+
+        if method == "exponential":
+            step = (1 / start_temp) ** (1 / num_iters)
+        else:
+            step = (start_temp - 1) / num_iters
 
         logger.info(f"Autofit start_temp {start_temp:.2f}, step {step:.2f}.")
 
-        return cls(start_temp, 1, step, method="exponential")
+        return cls(start_temp, 1, step, method=method)

alns/accept/tests/test_simulated_annealing.py

@@ -65,7 +65,10 @@ def test_does_not_raise():
     These sets of parameters should work correctly.
     """
     SimulatedAnnealing(10, 5, 1, "exponential")
+    SimulatedAnnealing(10, 5, 1, "EXPONENTIAL")
+
     SimulatedAnnealing(10, 5, 2, "linear")
+    SimulatedAnnealing(10, 5, 2, "LINEAR")
 
 
 @mark.parametrize("step", range(10))
@@ -171,7 +174,8 @@ def random(self):  # pylint: disable=no-self-use
         (-1, 0.5, 10),  # negative worse
         (0, -1, 10),  # negative prob
         (1.5, 0.5, 10),  # worse outside unit interval
-        (1, 0.9, -10),
+        (1, 0.9, -10),  # negative iterations
+        (1, 0.9, 0),  # zero iterations
     ],
 )  # negative number of iterations
 def test_autofit_raises_for_invalid_inputs(
@@ -206,3 +210,14 @@ def test_autofit_on_several_examples(
     assert_almost_equal(sa.end_temperature, sa_end)
     assert_almost_equal(sa.step, sa_step)
     assert_equal(sa.method, "exponential")
+
+
+def test_linear_autofit():
+    sa = SimulatedAnnealing.autofit(100, 0.05, 0.5, 100, "linear")
+    sa_start = -0.05 * 100 / np.log(0.5)
+    sa_step = (sa_start - 1) / 100
+
+    assert_almost_equal(sa.start_temperature, sa_start)
+    assert_almost_equal(sa.end_temperature, 1)
+    assert_almost_equal(sa.step, sa_step)
+    assert_equal(sa.method, "linear")