Interplay of Discrete Optimization and Machine Learning".
This article collection (Research Topic) will be published in
collaboration with Frontiers in Applied Mathematics and Statistics.
The aim of the Research Topic is to bring together the latest quality
articles from researchers working in the areas of discrete optimization
and machine learning, focused on discrete optimization methods that
address modern challenges in machine learning or machine learning for
improving discrete solvers or heuristics. The RT is edited by Elias
Khalil, Gonzalo Munoz, Sebastian Pokutta and myself (Andreas Tillmann).
All research will be published OpenAccess; submission will undergo full
peer review.
You can learn more about the scope of the collection here:
https://www.frontiersin.org/research-topics/47207/the-interplay-of-discrete-optimization-and-machine-learning
The current manuscript submission deadline is January 6th, 2023.
However, an extension is likely and we can also accommodate personal
extensions on a case-by-case basis.
If you would like feedback on your prospective manuscript's suitability,
you can submit an (informal) abstract at any time, which will be
assessed quickly.
Please note that as an Open-Access Journal, publishing fees are applied
to accepted articles, but the team at Frontiers is happy to advise you
in this regard. You can reach out to our dedicated point of contact at
Frontiers, Alexandra Powell, BSc via
mathematics.submissions@frontiersin.org if you have any questions.
If you are interested, you can register via the "Participate in this
topic" link on the special issue's website. You will then also receive a
follow-up email with further details.
Please feel free to forward this call for papers on to any possibly
interested colleagues.
--
Dr. Andreas M. Tillmann
Institute for Mathematical Optimization
Cluster of Excellence SE²A - Sustainable and Energy-Efficient Aviation
TU Braunschweig, Germany
https://www.tu-braunschweig.de/en/mo/team/tillmann
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