The Algorithm Configuration Problem
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911787732959232 |
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| author | Iommazzo, Gabriele D'Ambrosio, Claudia Frangioni, Antonio Liberti, Leo |
| author_facet | Iommazzo, Gabriele D'Ambrosio, Claudia Frangioni, Antonio Liberti, Leo |
| contents | The field of algorithmic optimization has significantly advanced with the development of methods for the automatic configuration of algorithmic parameters. This article delves into the Algorithm Configuration Problem, focused on optimizing parametrized algorithms for solving specific instances of decision/optimization problems. We present a comprehensive framework that not only formalizes the Algorithm Configuration Problem, but also outlines different approaches for its resolution, leveraging machine learning models and heuristic strategies. The article categorizes existing methodologies into per-instance and per-problem approaches, distinguishing between offline and online strategies for model construction and deployment. By synthesizing these approaches, we aim to provide a clear pathway for both understanding and addressing the complexities inherent in algorithm configuration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00898 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Algorithm Configuration Problem Iommazzo, Gabriele D'Ambrosio, Claudia Frangioni, Antonio Liberti, Leo Artificial Intelligence Machine Learning Optimization and Control The field of algorithmic optimization has significantly advanced with the development of methods for the automatic configuration of algorithmic parameters. This article delves into the Algorithm Configuration Problem, focused on optimizing parametrized algorithms for solving specific instances of decision/optimization problems. We present a comprehensive framework that not only formalizes the Algorithm Configuration Problem, but also outlines different approaches for its resolution, leveraging machine learning models and heuristic strategies. The article categorizes existing methodologies into per-instance and per-problem approaches, distinguishing between offline and online strategies for model construction and deployment. By synthesizing these approaches, we aim to provide a clear pathway for both understanding and addressing the complexities inherent in algorithm configuration. |
| title | The Algorithm Configuration Problem |
| topic | Artificial Intelligence Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2403.00898 |