# The Algorithm Configuration Problem

arXiv: https://arxiv.org/abs/2403.00898 · HTML: https://mlsanity.com/paper/2403.00898/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 1 Mar 2024.

## Builds on (most cited first, 1 of 1)

- [Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering](https://mlsanity.com/paper/2012.15151/) (2020)

## Built on by (most cited first, 1 of 1)

- [When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling](https://mlsanity.com/paper/2604.01886/) (2026)

## Abstract (arXiv)

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.