CORDIS Project
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This project focuses on developing advanced algorithms to efficiently approximate high-dimensional functions, addressing challenges in areas like financial engineering and machine learning. By combining multilevel Monte Carlo methods with stochastic gradient descent, it aims to overcome computational limitations and im…
In a series of relevant real world problems it is of fundamental importance to approximatively compute evaluations of high-dimensional functions.
Such high-dimensional approximation problems appear, e.g., in stochastic optimal control problems in operations research, e.g., in supervised learning problems, e.g., in financial engineering where partial differential equations (PDEs) and forward backward stochastic differential equations (FBSDEs) are used to approximatively price financial products,…
UNIVERSITAET MUENSTER
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