SeaPearl: A Constraint Programming Solver guided by Reinforcement\n Learning Article Swipe
Félix Chalumeau
,
Ilan Coulon
,
Quentin Cappart
,
Louis-Martin Rousseau
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.09193
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.09193
The design of efficient and generic algorithms for solving combinatorial\noptimization problems has been an active field of research for many years.\nStandard exact solving approaches are based on a clever and complete\nenumeration of the solution set. A critical and non-trivial design choice with\nsuch methods is the branching strategy, directing how the search is performed.\nThe last decade has shown an increasing interest in the design of machine\nlearning-based heuristics to solve combinatorial optimization problems. The\ngoal is to leverage knowledge from historical data to solve similar new\ninstances of a problem. Used alone, such heuristics are only able to provide\napproximate solutions efficiently, but cannot prove optimality nor bounds on\ntheir solution. Recent works have shown that reinforcement learning can be\nsuccessfully used for driving the search phase of constraint programming (CP)\nsolvers. However, it has also been shown that this hybridization is challenging\nto build, as standard CP frameworks do not natively include machine learning\nmechanisms, leading to some sources of inefficiencies. This paper presents the\nproof of concept for SeaPearl, a new CP solver implemented in Julia, that\nsupports machine learning routines in order to learn branching decisions using\nreinforcement learning. Support for modeling the learning component is also\nprovided. We illustrate the modeling and solution performance of this new\nsolver on two problems. Although not yet competitive with industrial solvers,\nSeaPearl aims to provide a flexible and open-source framework in order to\nfacilitate future research in the hybridization of constraint programming and\nmachine learning.\n
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2102.09193
- https://arxiv.org/pdf/2102.09193
- OA Status
- green
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287326329
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https://openalex.org/W4287326329Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2102.09193Digital Object Identifier
- Title
-
SeaPearl: A Constraint Programming Solver guided by Reinforcement\n LearningWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-02-18Full publication date if available
- Authors
-
Félix Chalumeau, Ilan Coulon, Quentin Cappart, Louis-Martin RousseauList of authors in order
- Landing page
-
https://arxiv.org/abs/2102.09193Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2102.09193Direct link to full text PDF
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-
YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2102.09193Direct OA link when available
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Reinforcement learning, Solver, Heuristics, Computer science, Leverage (statistics), Constraint programming, Mathematical optimization, Theoretical computer science, Artificial intelligence, Mathematics, Programming language, Stochastic programming, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.from | 76 |
| abstract_inverted_index.have | 107 |
| abstract_inverted_index.last | 53 |
| abstract_inverted_index.many | 19 |
| abstract_inverted_index.only | 91 |
| abstract_inverted_index.set. | 34 |
| abstract_inverted_index.some | 148 |
| abstract_inverted_index.such | 88 |
| abstract_inverted_index.that | 109, 130 |
| abstract_inverted_index.this | 131, 195 |
| abstract_inverted_index.used | 114 |
| abstract_inverted_index.with | 204 |
| abstract_inverted_index.based | 25 |
| abstract_inverted_index.exact | 21 |
| abstract_inverted_index.field | 15 |
| abstract_inverted_index.learn | 174 |
| abstract_inverted_index.order | 172, 216 |
| abstract_inverted_index.paper | 153 |
| abstract_inverted_index.phase | 119 |
| abstract_inverted_index.prove | 99 |
| abstract_inverted_index.shown | 56, 108, 129 |
| abstract_inverted_index.solve | 67, 80 |
| abstract_inverted_index.works | 106 |
| abstract_inverted_index.Julia, | 166 |
| abstract_inverted_index.Recent | 105 |
| abstract_inverted_index.active | 14 |
| abstract_inverted_index.alone, | 87 |
| abstract_inverted_index.bounds | 102 |
| abstract_inverted_index.build, | 135 |
| abstract_inverted_index.cannot | 98 |
| abstract_inverted_index.choice | 40 |
| abstract_inverted_index.clever | 28 |
| abstract_inverted_index.decade | 54 |
| abstract_inverted_index.design | 1, 39, 62 |
| abstract_inverted_index.future | 218 |
| abstract_inverted_index.search | 50, 118 |
| abstract_inverted_index.solver | 163 |
| abstract_inverted_index.Support | 179 |
| abstract_inverted_index.concept | 157 |
| abstract_inverted_index.driving | 116 |
| abstract_inverted_index.generic | 5 |
| abstract_inverted_index.include | 143 |
| abstract_inverted_index.leading | 146 |
| abstract_inverted_index.machine | 144, 168 |
| abstract_inverted_index.methods | 42 |
| abstract_inverted_index.provide | 209 |
| abstract_inverted_index.similar | 81 |
| abstract_inverted_index.solving | 8, 22 |
| abstract_inverted_index.sources | 149 |
| abstract_inverted_index.Although | 200 |
| abstract_inverted_index.However, | 124 |
| abstract_inverted_index.critical | 36 |
| abstract_inverted_index.flexible | 211 |
| abstract_inverted_index.interest | 59 |
| abstract_inverted_index.learning | 111, 169, 183 |
| abstract_inverted_index.leverage | 74 |
| abstract_inverted_index.modeling | 181, 190 |
| abstract_inverted_index.natively | 142 |
| abstract_inverted_index.presents | 154 |
| abstract_inverted_index.problem. | 85 |
| abstract_inverted_index.problems | 10 |
| abstract_inverted_index.research | 17, 219 |
| abstract_inverted_index.routines | 170 |
| abstract_inverted_index.solution | 33, 192 |
| abstract_inverted_index.standard | 137 |
| abstract_inverted_index.SeaPearl, | 159 |
| abstract_inverted_index.The\ngoal | 71 |
| abstract_inverted_index.branching | 45, 175 |
| abstract_inverted_index.component | 184 |
| abstract_inverted_index.decisions | 176 |
| abstract_inverted_index.directing | 47 |
| abstract_inverted_index.efficient | 3 |
| abstract_inverted_index.framework | 214 |
| abstract_inverted_index.knowledge | 75 |
| abstract_inverted_index.learning. | 178 |
| abstract_inverted_index.on\ntheir | 103 |
| abstract_inverted_index.problems. | 70, 199 |
| abstract_inverted_index.solution. | 104 |
| abstract_inverted_index.solutions | 95 |
| abstract_inverted_index.strategy, | 46 |
| abstract_inverted_index.algorithms | 6 |
| abstract_inverted_index.approaches | 23 |
| abstract_inverted_index.constraint | 121, 224 |
| abstract_inverted_index.frameworks | 139 |
| abstract_inverted_index.heuristics | 65, 89 |
| abstract_inverted_index.historical | 77 |
| abstract_inverted_index.illustrate | 188 |
| abstract_inverted_index.increasing | 58 |
| abstract_inverted_index.industrial | 205 |
| abstract_inverted_index.optimality | 100 |
| abstract_inverted_index.the\nproof | 155 |
| abstract_inverted_index.with\nsuch | 41 |
| abstract_inverted_index.competitive | 203 |
| abstract_inverted_index.implemented | 164 |
| abstract_inverted_index.learning.\n | 227 |
| abstract_inverted_index.new\nsolver | 196 |
| abstract_inverted_index.non-trivial | 38 |
| abstract_inverted_index.open-source | 213 |
| abstract_inverted_index.performance | 193 |
| abstract_inverted_index.programming | 122, 225 |
| abstract_inverted_index.and\nmachine | 226 |
| abstract_inverted_index.efficiently, | 96 |
| abstract_inverted_index.optimization | 69 |
| abstract_inverted_index.combinatorial | 68 |
| abstract_inverted_index.hybridization | 132, 222 |
| abstract_inverted_index.reinforcement | 110 |
| abstract_inverted_index.(CP)\nsolvers. | 123 |
| abstract_inverted_index.new\ninstances | 82 |
| abstract_inverted_index.that\nsupports | 167 |
| abstract_inverted_index.to\nfacilitate | 217 |
| abstract_inverted_index.also\nprovided. | 186 |
| abstract_inverted_index.challenging\nto | 134 |
| abstract_inverted_index.inefficiencies. | 151 |
| abstract_inverted_index.performed.\nThe | 52 |
| abstract_inverted_index.be\nsuccessfully | 113 |
| abstract_inverted_index.years.\nStandard | 20 |
| abstract_inverted_index.solvers,\nSeaPearl | 206 |
| abstract_inverted_index.provide\napproximate | 94 |
| abstract_inverted_index.using\nreinforcement | 177 |
| abstract_inverted_index.complete\nenumeration | 30 |
| abstract_inverted_index.learning\nmechanisms, | 145 |
| abstract_inverted_index.machine\nlearning-based | 64 |
| abstract_inverted_index.combinatorial\noptimization | 9 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.29721152 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |