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clone_model_sample_sat

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

First, you must install ortools package in this colab.

python

%pip install ortools

Showcases deep copying of a model.

python

import copy

from ortools.sat.python import cp_model


def clone_model_sample_sat():
    """Showcases cloning a model."""
    # Creates the model.
    model = cp_model.CpModel()

    # Creates the variables.
    num_vals = 3
    x = model.new_int_var(0, num_vals - 1, "x")
    y = model.new_int_var(0, num_vals - 1, "y")
    z = model.new_int_var(0, num_vals - 1, "z")

    # Creates the constraints.
    model.add(x != y)

    model.maximize(x + 2 * y + 3 * z)

    # Creates a solver and solves.
    solver = cp_model.CpSolver()
    status = solver.solve(model)

    if status == cp_model.OPTIMAL:
        print("Optimal value of the original model: {}".format(solver.objective_value))

    # Creates a dictionary holding the model and the variables you want to use.
    to_clone = {
        "model": model,
        "x": x,
        "y": y,
        "z": z,
    }

    # Deep copy the dictionary.
    clone = copy.deepcopy(to_clone)

    # Retrieve the cloned model and variables.
    cloned_model: cp_model.CpModel = clone["model"]
    cloned_x = clone["x"]
    cloned_y = clone["y"]
    cloned_model.add(cloned_x + cloned_y <= 1)

    status = solver.solve(cloned_model)

    if status == cp_model.OPTIMAL:
        print("Optimal value of the modified model: {}".format(solver.objective_value))


clone_model_sample_sat()