An algorithm for the calculation of upper variance under multiple probabilities and its application to quadratic programming

Xinpeng Li , Miao Yu , Shiyi Zheng

Probability, Uncertainty and Quantitative Risk ›› 2025, Vol. 10 ›› Issue (1) : 59 -66.

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Probability, Uncertainty and Quantitative Risk ›› 2025, Vol. 10 ›› Issue (1) : 59 -66. DOI: 10.3934/puqr.2025004

An algorithm for the calculation of upper variance under multiple probabilities and its application to quadratic programming

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Abstract

The concept of upper variance under multiple probabilities is defined through a corresponding minimax optimization problem. This study proposes a simple algorithm to solve this optimization problem exactly. Additionally, we provide a probabilistic representation for a class of quadratic programming problems, demonstrating the practical application of our approach.

Keywords

Multiple probabilities / Quadratic programming / Sublinear expectation / Upper variance

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Xinpeng Li, Miao Yu, Shiyi Zheng. An algorithm for the calculation of upper variance under multiple probabilities and its application to quadratic programming. Probability, Uncertainty and Quantitative Risk, 2025, 10(1): 59-66 DOI:10.3934/puqr.2025004

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Acknowledgements

This work is supported by the NSF of China (Grant Nos. 12326603 and 11601281), NSF of Shandong Province (Grant No. ZR2021MA018), National Key R&D Program of China (Grant No.2018YFA0703900), the Qilu Young Scholars Program of Shandong University, and the Fundamental Research Funds for the Central Universities.

References

[1]

Li, S., Li, X. and Yuan, G. X., Upper and lower variances under model uncertainty and their applications in finance, International Journal of Financial Engineering, 2022, 9(1): 2250007.

[2]

Nesterov, Y., Introductory Lectures on Convex Optimization: A Basic Course, Springer, New York, 2003.

[3]

Nesterov, Y. and Nemirovskii, A., Interior-Point Polynomial Algorithms in Convex Programming, Society for Industrial and Applied Mathematics, 1987

[4]

Pei, Z., Wang, X., Xu, Y. and Yue, X., A worst-case risk measure by G-VaR, Acta Mathematicae Applicatae Sinica, English Series, 2021, 37(2): 421-440.

[5]

Peng, S., Nonlinear Expectations and Stochastic Calculus under Uncertainty, Springer, Berlin, Heidelberg, 2019.

[6]

Peng, S., Yang, S. and Yao, J., Improving value-at-risk prediction under model uncertainty, Journal of Financial Econometrics, 2023, 21: 228-259.

[7]

Sion, M., On general minimax theorems, Pacific Journal of Mathematics, 1958, 8(1): 171-176.

[8]

Walley, P., Statistical Reasoning with Imprecise Probabilities, Chapman and Hall, 1991.

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