Summary of The Algorithm Configuration Problem, by Gabriele Iommazzo et al.
The Algorithm Configuration Problem
by Gabriele Iommazzo, Claudia D’Ambrosio, Antonio Frangioni, Leo Liberti
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 The paper presents a comprehensive framework for optimizing parametrized algorithms used to solve decision/optimization problems. The Algorithm Configuration Problem is formalized, along with different approaches for its resolution using machine learning models and heuristic strategies. Existing methodologies are categorized into per-instance and per-problem approaches, distinguishing between offline and online strategies for model construction and deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how to optimize algorithms by automatically setting their parameters. It shows how to use machine learning and other techniques to solve problems more effectively. The framework categorizes different ways of solving this problem, helping us navigate the complexities involved. |
Keywords
* Artificial intelligence * Machine learning * Optimization
