e179c8b847
* remove python script * remove RTE actions * fix test_xpress_interface.cc * remove callback_xpress.py * revert writing colnames and rownames * accept suggestion from Mizux * clean * change cmake/README.md * try fix build bazel * try fix build bazel add MPSWriteError.h * xpress tests gracefully exit if Xpress not found * add integer and linear programming test for dotnet python and java * remove MPSWriteError * try fix Window build * remove useless line from CMakeLists.txt * try fix test under windows * reformat * use XPRESS_LP instead of XPRESS for linear programming examples * tools: add --platform arg when possible make script more resilient/cross-platform * [CP-SAT] convert to PEP8 convention * use XPRSmipoptimize and XPRSlpoptimize instead of XPRSminim and XPRSmaxim (#114) * use XPRSmipoptimize and XPRSlpoptimize instead of XPRSminim and XPRSmaxim * clean xpress/environment files * accept changes: empty char* parameter for XPRS*optimize * Add test on number iterations with LP basis * fix gtests flags * refactor * suggestions by @flomnes * remove unwanted files --------- Co-authored-by: Andrea Sgattoni <andrea.sgattoni@rte-france.com> Co-authored-by: Laurent Perron <lperron@google.com> Co-authored-by: Corentin Le Molgat <corentinl@google.com> Co-authored-by: Andrea Sgattoni <andrea.sgattoni@gmail.com>
108 lines
3.3 KiB
Python
108 lines
3.3 KiB
Python
#!/usr/bin/env python3
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# Copyright 2010-2022 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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"""CP/SAT model for the N-queens problem."""
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import time
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from absl import app
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from absl import flags
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from ortools.sat.python import cp_model
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_SIZE = flags.DEFINE_integer("size", 8, "Number of queens.")
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class NQueenSolutionPrinter(cp_model.CpSolverSolutionCallback):
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"""Print intermediate solutions."""
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def __init__(self, queens: list[cp_model.IntVar]):
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cp_model.CpSolverSolutionCallback.__init__(self)
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self.__queens = queens
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self.__solution_count = 0
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self.__start_time = time.time()
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@property
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def solution_count(self) -> int:
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return self.__solution_count
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def on_solution_callback(self) -> None:
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current_time = time.time()
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print(
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"Solution %i, time = %f s"
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% (self.__solution_count, current_time - self.__start_time)
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)
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self.__solution_count += 1
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all_queens = range(len(self.__queens))
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for i in all_queens:
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for j in all_queens:
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if self.value(self.__queens[j]) == i:
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# There is a queen in column j, row i.
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print("Q", end=" ")
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else:
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print("_", end=" ")
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print()
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print()
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def main(_):
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board_size = _SIZE.value
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### Creates the solver.
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model = cp_model.CpModel()
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### Creates the variables.
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# The array index is the column, and the value is the row.
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queens = [
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model.new_int_var(0, board_size - 1, "x%i" % i) for i in range(board_size)
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]
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### Creates the constraints.
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# All columns must be different because the indices of queens are all
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# different, so we just add the all different constraint on the rows.
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model.add_all_different(queens)
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# No two queens can be on the same diagonal.
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diag1 = []
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diag2 = []
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for i in range(board_size):
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q1 = model.new_int_var(0, 2 * board_size, "diag1_%i" % i)
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q2 = model.new_int_var(-board_size, board_size, "diag2_%i" % i)
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diag1.append(q1)
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diag2.append(q2)
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model.add(q1 == queens[i] + i)
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model.add(q2 == queens[i] - i)
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model.add_all_different(diag1)
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model.add_all_different(diag2)
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### Solve model.
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solver = cp_model.CpSolver()
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solution_printer = NQueenSolutionPrinter(queens)
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# Enumerate all solutions.
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solver.parameters.enumerate_all_solutions = True
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# solve.
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solver.solve(model, solution_printer)
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print()
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print("Statistics")
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print(" - conflicts : %i" % solver.num_conflicts)
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print(" - branches : %i" % solver.num_branches)
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print(" - wall time : %f s" % solver.wall_time)
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print(" - solutions found : %i" % solution_printer.solution_count)
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if __name__ == "__main__":
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app.run(main)
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