b027e57e95
- Currently not implemented... Add abseil patch - Add patches/absl-config.cmake Makefile: Add abseil-cpp on unix - Force abseil-cpp SHA1 to 45221cc note: Just before the PR #136 which break all CMake Makefile: Add abseil-cpp on windows - Force abseil-cpp SHA1 to 45221cc note: Just before the PR #136 which break all CMake CMake: Add abseil-cpp - Force abseil-cpp SHA1 to 45221cc note: Just before the PR #136 which break all CMake port to absl: C++ Part - Fix warning with the use of ABSL_MUST_USE_RESULT > The macro must appear as the very first part of a function declaration or definition: ... Note: past advice was to place the macro after the argument list. src: dependencies/sources/abseil-cpp-master/absl/base/attributes.h:418 - Rename enum after windows clash - Remove non compact table constraints - Change index type from int64 to int in routing library - Fix file_nonport compilation on windows - Fix another naming conflict with windows (NO_ERROR is a macro) - Cleanup hash containers; work on sat internals - Add optional_boolean sub-proto Sync cpp examples with internal code - reenable issue173 after reducing number of loops port to absl: Python Part - Add back cp_model.INT32_MIN|MAX for examples Update Python examples - Add random_tsp.py - Run words_square example - Run magic_square in python tests port to absl: Java Part - Fix compilation of the new routing parameters in java - Protect some code from SWIG parsing Update Java Examples port to absl: .Net Part Update .Net examples work on sat internals; Add C++ CP-SAT CpModelBuilder API; update sample code and recipes to use the new API; sync with internal code Remove VS 2015 in Appveyor-CI - abseil-cpp does not support VS 2015... improve tables upgrade C++ sat examples to use the new API; work on sat internals update license dates rewrite jobshop_ft06_distance.py to use the CP-SAT solver rename last example revert last commit more work on SAT internals fix
124 lines
3.0 KiB
Python
124 lines
3.0 KiB
Python
# Copyright 2010 Hakan Kjellerstrand hakank@gmail.com
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#
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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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"""
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Lectures problem in Google CP Solver.
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Biggs: Discrete Mathematics (2nd ed), page 187.
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'''
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Suppose we wish to schedule six one-hour lectures, v1, v2, v3, v4, v5, v6.
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Among the the potential audience there are people who wish to hear both
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- v1 and v2
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- v1 and v4
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- v3 and v5
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- v2 and v6
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- v4 and v5
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- v5 and v6
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- v1 and v6
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How many hours are necessary in order that the lectures can be given
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without clashes?
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'''
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Compare with the following models:
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* MiniZinc: http://www.hakank.org/minizinc/lectures.mzn
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* SICstus: http://hakank.org/sicstus/lectures.pl
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* ECLiPSe: http://hakank.org/eclipse/lectures.ecl
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* Gecode: http://hakank.org/gecode/lectures.cpp
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This model was created by Hakan Kjellerstrand (hakank@gmail.com)
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Also see my other Google CP Solver models:
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http://www.hakank.org/google_or_tools/
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"""
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from __future__ import print_function
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import sys
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from ortools.constraint_solver import pywrapcp
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def main():
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# Create the solver.
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solver = pywrapcp.Solver('Lectures')
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#
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# data
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#
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#
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# The schedule requirements:
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# lecture a cannot be held at the same time as b
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# Note: 1-based
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g = [[1, 2], [1, 4], [3, 5], [2, 6], [4, 5], [5, 6], [1, 6]]
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# number of nodes
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n = 6
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# number of edges
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edges = len(g)
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#
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# declare variables
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#
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v = [solver.IntVar(0, n - 1, 'v[%i]' % i) for i in range(n)]
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# maximum color, to minimize
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# Note: since Python is 0-based, the
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# number of colors is +1
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max_c = solver.IntVar(0, n - 1, 'max_c')
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#
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# constraints
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#
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solver.Add(max_c == solver.Max(v))
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# ensure that there are no clashes
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# also, adjust to 0-base
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for i in range(edges):
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solver.Add(v[g[i][0] - 1] != v[g[i][1] - 1])
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# symmetry breaking:
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# - v0 has the color 0,
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# - v1 has either color 0 or 1
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solver.Add(v[0] == 0)
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solver.Add(v[1] <= 1)
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# objective
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objective = solver.Minimize(max_c, 1)
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#
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# solution and search
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#
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db = solver.Phase(v, solver.CHOOSE_MIN_SIZE_LOWEST_MIN,
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solver.ASSIGN_CENTER_VALUE)
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solver.NewSearch(db, [objective])
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num_solutions = 0
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while solver.NextSolution():
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num_solutions += 1
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print('max_c:', max_c.Value() + 1, 'colors')
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print('v:', [v[i].Value() for i in range(n)])
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print()
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print('num_solutions:', num_solutions)
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print('failures:', solver.Failures())
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print('branches:', solver.Branches())
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print('WallTime:', solver.WallTime(), 'ms')
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if __name__ == '__main__':
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main()
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