Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning Article Swipe
Swann Bessa
,
Darius Dabert
,
Max Bourgeat
,
Louis-Martin Rousseau
,
Quentin Cappart
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.12695
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2408.12695
Lagrangian decomposition (LD) is a relaxation method that provides a dual bound for constrained optimization problems by decomposing them into more manageable sub-problems. This bound can be used in branch-and-bound algorithms to prune the search space effectively. In brief, a vector of Lagrangian multipliers is associated with each sub-problem, and an iterative procedure (e.g., a sub-gradient optimization) adjusts these multipliers to find the tightest bound. Initially applied to integer programming, Lagrangian decomposition also had success in constraint programming due to its versatility and the fact that global constraints provide natural sub-problems. However, the non-linear and combinatorial nature of sub-problems in constraint programming makes it computationally intensive to optimize the Lagrangian multipliers with sub-gradient methods at each node of the tree search. This currently limits the practicality of LD as a general bounding mechanism for constraint programming. To address this challenge, we propose a self-supervised learning approach that leverages neural networks to generate multipliers directly, yielding tight bounds. This approach significantly reduces the number of sub-gradient optimization steps required, enhancing the pruning efficiency and reducing the execution time of constraint programming solvers. This contribution is one of the few that leverage learning to enhance bounding mechanisms on the dual side, a critical element in the design of combinatorial solvers. To our knowledge, this work presents the first generic method for learning valid dual bounds in constraint programming.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2408.12695
- https://arxiv.org/pdf/2408.12695
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4402698797Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2408.12695Digital Object Identifier
- Title
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Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised LearningWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-08-22Full publication date if available
- Authors
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Swann Bessa, Darius Dabert, Max Bourgeat, Louis-Martin Rousseau, Quentin CappartList of authors in order
- Landing page
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https://arxiv.org/abs/2408.12695Publisher landing page
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https://arxiv.org/pdf/2408.12695Direct link to full text PDF
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- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2408.12695Direct OA link when available
- Concepts
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Dual (grammatical number), Constraint (computer-aided design), Decomposition, Lagrangian, Mathematical optimization, Computer science, Artificial intelligence, Constraint programming, Mathematics, Applied mathematics, Chemistry, Stochastic programming, Geometry, Literature, Organic chemistry, ArtTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.multipliers | 43, 59, 110, 152 |
| abstract_inverted_index.programming | 77, 101, 179 |
| abstract_inverted_index.versatility | 81 |
| abstract_inverted_index.contribution | 182 |
| abstract_inverted_index.effectively. | 36 |
| abstract_inverted_index.optimization | 14, 165 |
| abstract_inverted_index.practicality | 125 |
| abstract_inverted_index.programming, | 69 |
| abstract_inverted_index.programming. | 135, 225 |
| abstract_inverted_index.sub-gradient | 55, 112, 164 |
| abstract_inverted_index.sub-problem, | 48 |
| abstract_inverted_index.sub-problems | 98 |
| abstract_inverted_index.combinatorial | 95, 206 |
| abstract_inverted_index.decomposition | 1, 71 |
| abstract_inverted_index.optimization) | 56 |
| abstract_inverted_index.significantly | 159 |
| abstract_inverted_index.sub-problems. | 22, 90 |
| abstract_inverted_index.computationally | 104 |
| abstract_inverted_index.self-supervised | 143 |
| abstract_inverted_index.branch-and-bound | 29 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |