Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds

Xiaocheng Li
Xiaocheng Li
[email protected]
https://orcid.org/0000-0001-6155-9068
Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom;
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,
Yinyu Ye
Yinyu Ye
[email protected]
Department of Management Science and Engineering, Stanford University, Stanford, California 94305
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Xiaocheng Li

[email protected]

https://orcid.org/0000-0001-6155-9068

Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom;

Search for more papers by this author

,

Yinyu Ye

[email protected]

Department of Management Science and Engineering, Stanford University, Stanford, California 94305

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Published Online:30 Nov 2021https://doi.org/10.1287/opre.2021.2164

Supplemental Material

opre.2021.2164.sm1.pdf

cover image Operations Research

Volume 70, Issue 5

September-October 2022

Pages iii-vi, 2597-3033, C2-C3

Article Information

Supplemental Material

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Received:November 22, 2019
Accepted:April 21, 2021
Published Online:November 30, 2021

Copyright © 2021, INFORMS

Cite as

Xiaocheng Li, Yinyu Ye (2021) Online Linear Programming: Dual Convergence, New Algorithms, and Regret Bounds. Operations Research 70(5):2948-2966.

https://doi.org/10.1287/opre.2021.2164

Keywords