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math.OC2025
Convexity and strict convexity for compositional neural networks in high-dimensional optimal control
Lars Grüne, Konrad Kleinberg, Thomas Kruse +1
Neural networks (NNs) have emerged as powerful tools for solving high-dimensional optimal control problems. In particular, their compositional structure has been shown to enable ef…
math.OC2025
Separable Approximations of Optimal Value Functions and Their Representation by Neural Networks
Mario Sperl, Luca Saluzzi, Dante Kalise +1
The use of separable approximations is proposed to mitigate the curse of dimensionality related to the approximation of high-dimensional value functions in optimal control. The sep…