Learn to optimize—a brief overview
Most optimization problems of practical significance are typically solved by highly configurable parameterized algorithms. To achieve the best performance on a problem instance, a trial-and-error configuration process is required, which is very costly and even prohibitive for problems that are alrea...
Gespeichert in:
| Veröffentlicht in: | National science review Jg. 11; H. 8; S. nwae132 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
China
Oxford University Press
01.08.2024
|
| Schlagworte: | |
| ISSN: | 2095-5138, 2053-714X, 2053-714X |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Zusammenfassung: | Most optimization problems of practical significance are typically solved by highly configurable parameterized algorithms. To achieve the best performance on a problem instance, a trial-and-error configuration process is required, which is very costly and even prohibitive for problems that are already computationally intensive, e.g. optimization problems associated with machine learning tasks. In the past decades, many studies have been conducted to accelerate the tedious configuration process by learning from a set of training instances. This article refers to these studies as learn to optimize and reviews the progress achieved.
The article presents an overview on “Learn to Optimiz”, a paradigm that leverage on a set of training instances to accelerate the tedious configuration process of optimization algorithms. |
|---|---|
| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 |
| ISSN: | 2095-5138 2053-714X 2053-714X |
| DOI: | 10.1093/nsr/nwae132 |