Summary of Athanor: Local Search Over Abstract Constraint Specifications, by Saad Attieh et al.
Athanor: Local Search over Abstract Constraint Specifications
by Saad Attieh, Nguyen Dang, Christopher Jefferson, Ian Miguel, Peter Nightingale
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Local search solvers are used to solve complex optimization problems. Our research focuses on general-purpose solvers that can accept constraint models as input. We propose a new solver called Athanor that takes problem specifications written in the Essence language, which allows for abstract descriptions without low-level modeling decisions. This approach enables automatic generation of high-quality neighborhoods, avoiding manual structure identification. By leveraging these features, our empirical results show strong performance compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper discusses a new local search solver called Athanor that solves complex optimization problems. It’s different from other solvers because it starts with a problem description written in the Essence language. This language lets you describe problems without worrying about small details. The solver uses this information to create good neighborhoods, making it easier and faster to find solutions. Overall, Athanor performs well compared to other methods. |
Keywords
» Artificial intelligence » Optimization
