83 lines
2.2 KiB
Python
83 lines
2.2 KiB
Python
#!/usr/bin/env python3
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# Copyright 2010-2025 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Showcases deep copying of a model."""
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# [START program]
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import copy
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from ortools.sat.python import cp_model
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def clone_model_sample_sat():
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"""Showcases cloning a model."""
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# Creates the model.
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# [START model]
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model = cp_model.CpModel()
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# [END model]
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# Creates the variables.
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# [START variables]
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num_vals = 3
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x = model.new_int_var(0, num_vals - 1, "x")
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y = model.new_int_var(0, num_vals - 1, "y")
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z = model.new_int_var(0, num_vals - 1, "z")
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# [END variables]
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# Creates the constraints.
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# [START constraints]
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model.add(x != y)
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# [END constraints]
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# [START objective]
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model.maximize(x + 2 * y + 3 * z)
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# [END objective]
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# Creates a solver and solves.
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# [START solve]
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solver = cp_model.CpSolver()
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status = solver.solve(model)
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# [END solve]
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if status == cp_model.OPTIMAL:
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print("Optimal value of the original model: {}".format(solver.objective_value))
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# [START clone]
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# Creates a dictionary holding the model and the variables you want to use.
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to_clone = {
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"model": model,
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"x": x,
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"y": y,
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"z": z,
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}
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# Deep copy the dictionary.
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clone = copy.deepcopy(to_clone)
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# Retrieve the cloned model and variables.
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cloned_model: cp_model.CpModel = clone["model"]
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cloned_x = clone["x"]
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cloned_y = clone["y"]
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cloned_model.add(cloned_x + cloned_y <= 1)
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# [END clone]
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status = solver.solve(cloned_model)
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if status == cp_model.OPTIMAL:
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print("Optimal value of the modified model: {}".format(solver.objective_value))
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clone_model_sample_sat()
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# [END program]
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