highspy multiobjective

Author: quantresearch1

src/highspy/__init__.pyi

@@ -7,6 +7,7 @@ from highspy._core import (
     HighsHessian,
     HighsInfo,
     HighsInfoType,
+    HighsLinearObjective,
     HighsLogType,
     HighsLp,
     HighsModel,
@@ -59,6 +60,7 @@ __all__: list[str] = [
     "HighsRanging",
     "kHighsInf",
     "kHighsIInf",
+    "HighsLinearObjective",
     "HIGHS_VERSION_MAJOR",
     "HIGHS_VERSION_MINOR",
     "HIGHS_VERSION_PATCH",

src/highspy/_core/__init__.pyi

@@ -290,6 +290,17 @@ class HighsModel:
 
     def __init__(self) -> None: ...
 
+class HighsLinearObjective:
+    weight: float
+    offset: float
+    coefficients: list[float]
+    abs_tolerance: float
+    rel_tolerance: float
+    priority: int
+    
+    def __init__(self) -> None: ...
+
+
 class HighsModelStatus:
     """
     Members:

src/highspy/highs.py

@@ -10,6 +10,7 @@
     ObjSense,
     HighsVarType,
     HighsStatus,
+    HighsLinearObjective,
     cb,  # type: ignore
     _Highs,  # type: ignore
     readonly_ptr_wrapper_double,
@@ -249,6 +250,27 @@ def maximize(self, obj: Optional[Union[highs_var, highs_linear_expression]] = No
 
         super().changeObjectiveSense(ObjSense.kMaximize)
         return self.solve()
+    
+    def addLinearObjective(self, linear_objective: HighsLinearObjective):
+        """
+       Adds linear objective data to HiGHS.
+        
+        Args:
+            linear_objective: A HighsLinearObjective object representing the objective to add.
+            
+        Returns:
+            A HighsStatus object indicating whether the call succeeded..
+        """
+        return super().addLinearObjective(linear_objective)
+
+    def clearLinearObjectives(self):
+        """
+        Clears any multiple linear objective data in HiGHS.
+        
+        Returns:
+            A HighsStatus object indicating whether the call succeeded..
+        """
+        return super().clearLinearObjectives()
 
     @staticmethod
     def internal_get_value(

tests/test_highspy.py

@@ -2059,3 +2059,32 @@ def test_repr_str(self):
         expr = y + x <= 5
         self.assertEqual(repr(expr), "-inf <= 1.0_v1  1.0_v0 <= 5.0")
         self.assertEqual(str(expr), "-inf <= 1.0_v0  1.0_v1 <= 5.0")
+
+    def test_lexicographic_optimization(self):
+        # max f1 = X1
+        # max f2 = 3 X1 + 4 X2
+        # st  X1 <= 2
+        #     X2 <= 4
+        #     5 X1 + 4 X2 <= 20
+        model = highspy.Highs()
+        model.setOptionValue("blend_multi_objectives", False)
+        num_vars = 2
+        model.addVars(num_vars, np.array([0.0, 0.0]), np.array([2.0, 4.0]))
+        obj1 = highspy.HighsLinearObjective()
+        obj1.offset = 0
+        obj1.coefficients = [-1, 0]
+        obj1.priority = 1
+        obj1.abs_tolerance = 1
+        obj2 = highspy.HighsLinearObjective()
+        obj2.offset=0
+        obj2.coefficients=[-3, -4]
+        obj2.priority=2
+        model.addLinearObjective(obj1)
+        model.addLinearObjective(obj2)
+        model.addRow(0.0, 20.0, num_vars, np.arange(num_vars), np.array([5.0, 4.0]))
+        # Solve the model
+        model.run()
+
+        # Get the solution status
+        status = model.getModelStatus()
+        self.assertEqual(status, highspy.HighsModelStatus.kOptimal)