Update redundant_facet_removal_experiment.py

dingo/experimental_gurobi_functions/redundant_facet_removal_experiment.py

@@ -12,6 +12,44 @@
 from gurobipy import GRB
 import math
 
+def update_model_constraints_and_bounds(model, Aeq_sparse=None, beq=None, A_sparse=None, b=None,
+                                       new_lower_bounds=None, new_upper_bounds=None):
+    if Aeq_sparse is not None and beq is not None:
+        # Remove the old equality constraints
+        model.remove("c")
+
+        # Add new equality constraints
+        model.addMConstr(sp.csr_matrix(Aeq_sparse), model.getVars(), "=", np.array(beq), name="c")
+
+    if A_sparse is not None and b is not None:
+        # Remove the old inequality constraints
+        model.remove("d")
+
+        # Add new inequality constraints
+        model.addMConstr(sp.csr_matrix(A_sparse), model.getVars(), "<", np.array(b), name="d")
+
+    if new_lower_bounds is not None or new_upper_bounds is not None:
+        # Retrieve the variable objects
+        x_vars = model.getVars()
+
+        # Update variable bounds if specified
+        if new_lower_bounds is not None:
+            for i, x_var in enumerate(x_vars):
+                x_var.lb = new_lower_bounds[i]
+
+        if new_upper_bounds is not None:
+            for i, x_var in enumerate(x_vars):
+                x_var.ub = new_upper_bounds[i]
+
+    # Update the model to apply the changes
+    model.update()
+
+# Usage example:
+# Assuming you have a Gurobi model 'model' and new constraint matrices/vectors and bounds
+# Call the function to update the model
+# update_model_constraints_and_bounds(model, Aeq_sparse_new, beq_new, A_sparse_new, b_new, new_lower_bounds, new_upper_bounds)
+
+
 def solve_lp_with_different_objectives(model, new_objective_coeffs):
     """
     Solve a linear program with a different objective function.