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clone_model_mb

<table align="left"> <td> <a href="https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/linear_solver/clone_model_mb.ipynb">Run in Google Colab</a> </td> <td> <a href="https://github.com/google/or-tools/blob/main/ortools/linear_solver/samples/clone_model_mb.py">View source on GitHub</a> </td> </table>

First, you must install ortools package in this colab.

python

%pip install ortools

Integer programming examples that show how to clone a model.

python

import math

from ortools.linear_solver.python import model_builder



def main():
    # Create the model.
    model = model_builder.Model()

    # x and y are integer non-negative variables.
    x = model.new_int_var(0.0, math.inf, "x")
    y = model.new_int_var(0.0, math.inf, "y")

    # x + 7 * y <= 17.5.
    unused_c1 = model.add(x + 7 * y <= 17.5)

    # x <= 3.5.
    c2 = model.add(x <= 3.5)

    # Maximize x + 10 * y.
    model.maximize(x + 10 * y)

    # [Start clone]
    # Clone the model.
    print("Cloning the model.")
    model_copy = model.clone()
    x_copy = model_copy.var_from_index(x.index)
    y_copy = model_copy.var_from_index(y.index)
    z_copy = model_copy.new_bool_var("z")
    c2_copy = model_copy.linear_constraint_from_index(c2.index)

    # Add new constraint.
    model_copy.add(x_copy >= 1)
    print(f"Number of constraints in original model ={model.num_constraints}")
    print(f"Number of constraints in cloned model = {model_copy.num_constraints}")

    # Modify a constraint.
    c2_copy.add_term(z_copy, 2.0)

    # Create the solver with the SCIP backend, and solve the model.
    solver = model_builder.Solver("scip")
    if not solver.solver_is_supported():
        return
    status = solver.solve(model_copy)

    if status == model_builder.SolveStatus.OPTIMAL:
        print("Solution:")
        print(f"Objective value = {solver.objective_value}")
        print(f"x = {solver.value(x_copy)}")
        print(f"y = {solver.value(y_copy)}")
        print(f"z = {solver.value(z_copy)}")
    else:
        print("The problem does not have an optimal solution.")

    print("\nAdvanced usage:")
    print(f"Problem solved in {solver.wall_time} seconds")


main()