Multitask learning and nonlinear optimal control of the COVID-19 outbreak: A geometric programming approach

Mikhail Hayhoe, Francisco Barreras, Victor M. Preciado

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DOI: 10.1016/j.arcontrol.2021.04.014
Published: 2021
Container: Annual Reviews in Control
Publisher: Elsevier BV
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.arcontrol.2021.04.014,
  title = {Multitask learning and nonlinear optimal control of the COVID-19 outbreak: A geometric programming approach},
  author = {Mikhail Hayhoe and Francisco Barreras and Victor M. Preciado},
  year = {2021},
  journal = {Annual Reviews in Control},
  doi = {10.1016/j.arcontrol.2021.04.014},
  url = {https://doi.org/10.1016/j.arcontrol.2021.04.014}
}

RIS

TY  - JOUR
TI  - Multitask learning and nonlinear optimal control of the COVID-19 outbreak: A geometric programming approach
AU  - Mikhail Hayhoe
AU  - Francisco Barreras
AU  - Victor M. Preciado
PY  - 2021
JO  - Annual Reviews in Control
DO  - 10.1016/j.arcontrol.2021.04.014
UR  - https://doi.org/10.1016/j.arcontrol.2021.04.014
ER  -

APA

Hayhoe, M., Barreras, F., & Preciado, V. M. (2021). Multitask learning and nonlinear optimal control of the COVID-19 outbreak: A geometric programming approach. Annual Reviews in Control. https://doi.org/10.1016/j.arcontrol.2021.04.014

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crossref · retrieved 2026-09-25T15:05:56.255Z