Virtual Savant: Learning for Optimization
Abstract
This article describes Virtual Savant, a novel paradigm that applies machine learning to derive knowledge from previously-solved optimization problem instances in order to solve new ones in a massively-parallel fashion. Applications of Virtual Savant to two classic combinatorial optimization problems and to one real-world problem are presented and experimental results are discussed.
Cite
Text
Massobrio et al. "Virtual Savant: Learning for Optimization." NeurIPS 2020 Workshops: LMCA, 2020.Markdown
[Massobrio et al. "Virtual Savant: Learning for Optimization." NeurIPS 2020 Workshops: LMCA, 2020.](https://mlanthology.org/neuripsw/2020/massobrio2020neuripsw-virtual/)BibTeX
@inproceedings{massobrio2020neuripsw-virtual,
title = {{Virtual Savant: Learning for Optimization}},
author = {Massobrio, Renzo and Nesmachnow, Sergio and Dorronsoro, Bernabé},
booktitle = {NeurIPS 2020 Workshops: LMCA},
year = {2020},
url = {https://mlanthology.org/neuripsw/2020/massobrio2020neuripsw-virtual/}
}