channeling_sample_sat.py

#!/usr/bin/env python3
# Copyright 2010-2024 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Link integer constraints together."""


from ortools.sat.python import cp_model


class VarArraySolutionPrinter(cp_model.CpSolverSolutionCallback):
    """Print intermediate solutions."""

    def __init__(self, variables: list[cp_model.IntVar]):
        cp_model.CpSolverSolutionCallback.__init__(self)
        self.__variables = variables

    def on_solution_callback(self) -> None:
        for v in self.__variables:
            print(f"{v}={self.value(v)}", end=" ")
        print()


def channeling_sample_sat():
    """Demonstrates how to link integer constraints together."""

    # Create the CP-SAT model.
    model = cp_model.CpModel()

    # Declare our two primary variables.
    x = model.new_int_var(0, 10, "x")
    y = model.new_int_var(0, 10, "y")

    # Declare our intermediate boolean variable.
    b = model.new_bool_var("b")

    # Implement b == (x >= 5).
    model.add(x >= 5).only_enforce_if(b)
    model.add(x < 5).only_enforce_if(~b)

    # Create our two half-reified constraints.
    # First, b implies (y == 10 - x).
    model.add(y == 10 - x).only_enforce_if(b)
    # Second, not(b) implies y == 0.
    model.add(y == 0).only_enforce_if(~b)

    # Search for x values in increasing order.
    model.add_decision_strategy([x], cp_model.CHOOSE_FIRST, cp_model.SELECT_MIN_VALUE)

    # Create a solver and solve with a fixed search.
    solver = cp_model.CpSolver()

    # Force the solver to follow the decision strategy exactly.
    solver.parameters.search_branching = cp_model.FIXED_SEARCH
    # Enumerate all solutions.
    solver.parameters.enumerate_all_solutions = True

    # Search and print out all solutions.
    solution_printer = VarArraySolutionPrinter([x, y, b])
    solver.solve(model, solution_printer)


channeling_sample_sat()