Cubing for Tuning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910940458385408 |
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| author | Wu, Haoze Barrett, Clark Narodytska, Nina |
| author_facet | Wu, Haoze Barrett, Clark Narodytska, Nina |
| contents | We are exploring the problem of building an automated reasoning procedure that adaptively tunes the high-level solving strategy for a given problem. There are two main distinctive characteristics of our approach: tuning is performed solely online, unlike the common use of tuning as an offline process; and tuning data comes exclusively from the given instance, so we do not rely on the availability of similar benchmarks and can work with unique challenging instances. Our approach builds on top of the divide-and-conquer paradigm that naturally serves partitioned sub-problems for an automated tuning algorithm to obtain a good solving strategy. We demonstrate performance improvement on two classes of important problems--SAT-solving and neural network verification--and show that our method can learn unconventional solving strategies in some cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19039 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cubing for Tuning Wu, Haoze Barrett, Clark Narodytska, Nina Logic in Computer Science We are exploring the problem of building an automated reasoning procedure that adaptively tunes the high-level solving strategy for a given problem. There are two main distinctive characteristics of our approach: tuning is performed solely online, unlike the common use of tuning as an offline process; and tuning data comes exclusively from the given instance, so we do not rely on the availability of similar benchmarks and can work with unique challenging instances. Our approach builds on top of the divide-and-conquer paradigm that naturally serves partitioned sub-problems for an automated tuning algorithm to obtain a good solving strategy. We demonstrate performance improvement on two classes of important problems--SAT-solving and neural network verification--and show that our method can learn unconventional solving strategies in some cases. |
| title | Cubing for Tuning |
| topic | Logic in Computer Science |
| url | https://arxiv.org/abs/2504.19039 |