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>
115 lines
3.7 KiB
Python
115 lines
3.7 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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"""solve a random Weighted Latency problem with the CP-SAT solver."""
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import random
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from typing import Sequence
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from absl import app
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from absl import flags
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from google.protobuf import text_format
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from ortools.sat.python import cp_model
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_NUM_NODES = flags.DEFINE_integer("num_nodes", 12, "Number of nodes to visit.")
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_GRID_SIZE = flags.DEFINE_integer("grid_size", 20, "Size of the grid where nodes are.")
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_PROFIT_RANGE = flags.DEFINE_integer("profit_range", 50, "Range of profit.")
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_SEED = flags.DEFINE_integer("seed", 0, "Random seed.")
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_PARAMS = flags.DEFINE_string(
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"params", "num_search_workers:16, max_time_in_seconds:5", "Sat solver parameters."
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)
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_PROTO_FILE = flags.DEFINE_string(
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"proto_file", "", "If not empty, output the proto to this file."
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)
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def build_model():
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"""Create the nodes and the profit."""
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random.seed(_SEED.value)
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x = []
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y = []
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x.append(random.randint(0, _GRID_SIZE.value))
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y.append(random.randint(0, _GRID_SIZE.value))
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for _ in range(_NUM_NODES.value):
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x.append(random.randint(0, _GRID_SIZE.value))
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y.append(random.randint(0, _GRID_SIZE.value))
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profits = []
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profits.append(0)
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for _ in range(_NUM_NODES.value):
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profits.append(random.randint(1, _PROFIT_RANGE.value))
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sum_of_profits = sum(profits)
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profits = [p / sum_of_profits for p in profits]
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return x, y, profits
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def solve_with_cp_sat(x, y, profits):
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"""Solves the problem with the CP-SAT solver."""
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model = cp_model.CpModel()
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# because of the manhattan distance, the sum of distances is bounded by this.
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horizon = _GRID_SIZE.value * 2 * _NUM_NODES.value
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times = [
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model.new_int_var(0, horizon, f"x_{i}") for i in range(_NUM_NODES.value + 1)
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]
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# Node 0 is the start node.
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model.add(times[0] == 0)
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# Create the circuit constraint.
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arcs = []
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for i in range(_NUM_NODES.value + 1):
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for j in range(_NUM_NODES.value + 1):
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if i == j:
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continue
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# We use a manhattan distance between nodes.
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distance = abs(x[i] - x[j]) + abs(y[i] - y[j])
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lit = model.new_bool_var(f"{i}_to_{j}")
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arcs.append((i, j, lit))
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# add transitions between nodes.
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if i == 0:
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# Initial transition
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model.add(times[j] == distance).only_enforce_if(lit)
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elif j != 0:
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# We do not care for the last transition.
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model.add(times[j] == times[i] + distance).only_enforce_if(lit)
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model.add_circuit(arcs)
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model.minimize(cp_model.LinearExpr.weighted_sum(times, profits))
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if _PROTO_FILE.value:
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model.export_to_file(_PROTO_FILE.value)
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# Solve model.
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solver = cp_model.CpSolver()
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if _PARAMS.value:
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text_format.Parse(_PARAMS.value, solver.parameters)
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solver.parameters.log_search_progress = True
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solver.solve(model)
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def main(argv: Sequence[str]) -> None:
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if len(argv) > 1:
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raise app.UsageError("Too many command-line arguments.")
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x, y, profits = build_model()
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solve_with_cp_sat(x, y, profits)
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# TODO(user): Implement routing model.
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if __name__ == "__main__":
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app.run(main)
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