Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of output quantities of complex differential models with random input data, using gradient-based approaches in combination with the Multi-Level Monte Carlo (MLMC) method. In particular, we consider the framework o...

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Bibliographic Details

Published in:	Journal of computational physics Vol. 495; p. 112523
Main Authors:	Ganesh, Sundar, Nobile, Fabio
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 15.12.2023
Subjects:	CVaR Gradient descent Multilevel Monte Carlo methods Optimisation under uncertainty Uncertainty quantification VaR Uncertainty quantification Gradient descent CVaR Multilevel Monte Carlo methods VaR Optimisation under uncertainty
ISSN:	0021-9991, 1090-2716
Online Access:	Get full text
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