simply add multiobj to __init__.pyi

Author: quantresearch1

src/highspy/_core/__init__.pyi

@@ -788,6 +788,7 @@ class _Highs:
         indices: numpy.ndarray[typing.Any, numpy.dtype[numpy.int32]],
         values: numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]],
     ) -> HighsStatus: ...
+    def addLinearObjective(self, linear_objective: HighsLinearObjective) -> HighsStatus: ...
     def addVar(self, lower_bound: float, upper_bound: float) -> HighsStatus: ...
     def addVars(
         self,
@@ -825,6 +826,7 @@ class _Highs:
     def changeRowBounds(self, row: int, lower_bound: float, upper_bound: float) -> HighsStatus: ...
     def clear(self) -> HighsStatus: ...
     def clearModel(self) -> HighsStatus: ...
+    def clearLinearObjectives(self) -> HighsStatus: ...
     def clearSolver(self) -> HighsStatus: ...
     def crossover(self, solution: HighsSolution) -> HighsStatus: ...
     def deleteCols(self, num_cols: int, cols: numpy.ndarray[typing.Any, numpy.dtype[numpy.int32]]) -> HighsStatus: ...

src/highspy/highs.py

@@ -250,28 +250,6 @@ 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(
         array_values: Union[Sequence[float], np.ndarray[Any, np.dtype[np.float64]], readonly_ptr_wrapper_double],