MIPLearn v0.3

miplearn/components/primal/actions.py

@@ -0,0 +1,93 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020-2022, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+import logging
+from abc import ABC, abstractmethod
+from typing import Optional, Dict
+
+import numpy as np
+
+from miplearn.solvers.abstract import AbstractModel
+
+logger = logging.getLogger()
+
+
+class PrimalComponentAction(ABC):
+    @abstractmethod
+    def perform(
+        self,
+        model: AbstractModel,
+        var_names: np.ndarray,
+        var_values: np.ndarray,
+        stats: Optional[Dict],
+    ) -> None:
+        pass
+
+
+class SetWarmStart(PrimalComponentAction):
+    def perform(
+        self,
+        model: AbstractModel,
+        var_names: np.ndarray,
+        var_values: np.ndarray,
+        stats: Optional[Dict],
+    ) -> None:
+        logger.info("Setting warm starts...")
+        model.set_warm_starts(var_names, var_values, stats)
+
+
+class FixVariables(PrimalComponentAction):
+    def perform(
+        self,
+        model: AbstractModel,
+        var_names: np.ndarray,
+        var_values: np.ndarray,
+        stats: Optional[Dict],
+    ) -> None:
+        logger.info("Fixing variables...")
+        assert len(var_values.shape) == 2
+        assert var_values.shape[0] == 1
+        var_values = var_values.reshape(-1)
+        model.fix_variables(var_names, var_values, stats)
+        if stats is not None:
+            stats["Heuristic"] = True
+
+
+class EnforceProximity(PrimalComponentAction):
+    def __init__(self, tol: float) -> None:
+        self.tol = tol
+
+    def perform(
+        self,
+        model: AbstractModel,
+        var_names: np.ndarray,
+        var_values: np.ndarray,
+        stats: Optional[Dict],
+    ) -> None:
+        assert len(var_values.shape) == 2
+        assert var_values.shape[0] == 1
+        var_values = var_values.reshape(-1)
+
+        constr_lhs = []
+        constr_vars = []
+        constr_rhs = 0.0
+        for (i, var_name) in enumerate(var_names):
+            if np.isnan(var_values[i]):
+                continue
+            constr_lhs.append(1.0 if var_values[i] < 0.5 else -1.0)
+            constr_rhs -= var_values[i]
+            constr_vars.append(var_name)
+
+        constr_rhs += len(constr_vars) * self.tol
+        logger.info(
+            f"Adding proximity constraint (tol={self.tol}, nz={len(constr_vars)})..."
+        )
+
+        model.add_constrs(
+            np.array(constr_vars),
+            np.array([constr_lhs]),
+            np.array(["<"], dtype="S"),
+            np.array([constr_rhs]),
+        )
+        if stats is not None:
+            stats["Heuristic"] = True