4f381f6d07
* bump abseil to 20250814 * bump protobuf to v32.0 * cmake: add ccache auto support * backport flatzinc, math_opt and sat update
76 lines
2.4 KiB
Python
76 lines
2.4 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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# [START program]
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"""Link integer constraints together."""
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from ortools.sat.python import cp_model
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class VarArraySolutionPrinter(cp_model.CpSolverSolutionCallback):
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"""Print intermediate solutions."""
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def __init__(self, variables: list[cp_model.IntVar]):
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cp_model.CpSolverSolutionCallback.__init__(self)
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self.__variables = variables
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def on_solution_callback(self) -> None:
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for v in self.__variables:
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print(f"{v}={self.value(v)}", end=" ")
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print()
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def channeling_sample_sat():
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"""Demonstrates how to link integer constraints together."""
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# Create the CP-SAT model.
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model = cp_model.CpModel()
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# Declare our two primary variables.
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x = model.new_int_var(0, 10, "x")
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y = model.new_int_var(0, 10, "y")
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# Declare our intermediate boolean variable.
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b = model.new_bool_var("b")
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# Implement b == (x >= 5).
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model.add(x >= 5).only_enforce_if(b)
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model.add(x < 5).only_enforce_if(~b)
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# Create our two half-reified constraints.
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# First, b implies (y == 10 - x).
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model.add(y == 10 - x).only_enforce_if(b)
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# Second, not(b) implies y == 0.
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model.add(y == 0).only_enforce_if(~b)
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# Search for x values in increasing order.
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model.add_decision_strategy([x], cp_model.CHOOSE_FIRST, cp_model.SELECT_MIN_VALUE)
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# Create a solver and solve with a fixed search.
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solver = cp_model.CpSolver()
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# Force the solver to follow the decision strategy exactly.
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solver.parameters.search_branching = cp_model.FIXED_SEARCH
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# Enumerate all solutions.
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solver.parameters.enumerate_all_solutions = True
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# Search and print out all solutions.
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solution_printer = VarArraySolutionPrinter([x, y, b])
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solver.solve(model, solution_printer)
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channeling_sample_sat()
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# [END program]
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