DagRep.12.10.166.pdf
- Filesize: 2.56 MB
- 9 pages
Document
Data-Driven Combinatorial Optimisation (Dagstuhl Seminar 22431)
Authors and all authors of the abstracts in this report
-
Part of:
Issue:
Dagstuhl Reports, Volume 12, Issue 10
Volume: Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-05-03
Cite AsGet BibTex
Emma Frejinger, Andrea Lodi, Michele Lombardi, and Neil Yorke-Smith. Data-Driven Combinatorial Optimisation (Dagstuhl Seminar 22431). In Dagstuhl Reports, Volume 12, Issue 10, pp. 166-174, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/DagRep.12.10.166
https://doi.org/10.4230/DagRep.12.10.166
Abstract
Machine learning’s impressive achievements in the last decade have urged many scientific communities to ask if and how the techniques developed in that field to leverage data could be used to advance research in others. The combinatorial optimisation community is one of those, and the area of data-driven combinatorial optimisation has emerged. The motivation of the seminar and its design and development have followed the idea of making researchers both in academia and industry belonging to different communities - from operations research to constraint programming, from artificial intelligence to machine learning - communicate, establish a shared language, and ultimately (try to) set the roadmap for the development of the field.
Subject Classification
ACM Subject Classification
- Theory of computation → Constraint and logic programming
- Computing methodologies → Machine learning
- Theory of computation → Mathematical optimization
- Theory of computation → Reinforcement learning
Keywords
- combinatorial optimisation
- constraint programming
- machine learning
- Mixed integer programming
- operations research
- Reinforcement learning
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads