Optimization Under Severe Uncertainty: a Generalized Minimax Regret Approach for Problems with Linear Objectives

Author:

Vu Tuan-Anh,Afifi Sohaib,Lefèvre Éric,Pichon Frédéric

Publisher

Springer Nature Switzerland

Reference13 articles.

1. Adam, L., Destercke, S.: Possibilistic preference elicitation by minimax regret. In: Uncertainty in Artificial Intelligence, pp. 718–727. PMLR (2021)

2. Agarwal, A., Zhang, T.: Minimax regret optimization for robust machine learning under distribution shift. In: Conference on Learning Theory, pp. 2704–2729. PMLR (2022)

3. Al Taha, F., Yan, S., Bitar, E.: A distributionally robust approach to regret optimal control using the Wasserstein distance. In: 2023 62nd IEEE Conference on Decision and Control (CDC), pp. 2768–2775. IEEE (2023)

4. Augustin, T., Coolen, F.P., De Cooman, G., Troffaes, M.C.: Introduction to Imprecise Probabilities. John Wiley & Sons, Hoboken (2014)

5. Conde, E.: A 2-approximation for minmax regret problems via a mid-point scenario optimal solution. Oper. Res. Lett. 38(4), 326–327 (2010)

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