The Algorithm Configuration Problem
Gabriele Iommazzo Claudia D'Ambrosio Antonio Frangioni Leo Liberti
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.