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math.OC2026

Towards turnpike-based performance analysis of risk-averse stochastic predictive control

Jonas Schießl, Ruchuan Ou, Michael H. Baumann +2

In this paper, we present performance estimates for stochastic economic MPC schemes with risk-averse cost formulations. For MPC algorithms with costs given by expectations, it was…

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…

math.OC2025

Turnpike and dissipativity in generalized discrete-time stochastic linear-quadratic optimal control

Jonas Schießl, Ruchuan Ou, Timm Faulwasser +2

We investigate different turnpike phenomena of generalized discrete-time stochastic linear-quadratic optimal control problems. Our analysis is based on a novel strict dissipativity…

math.OC2025

A Polynomial Chaos Approach to Stochastic LQ Optimal Control: Error Bounds and Infinite-Horizon Results

Ruchuan Ou, Jonas Schießl, Michael Heinrich Baumann +2

The stochastic linear--quadratic regulator problem subject to Gaussian disturbances is well known and usually addressed via a moment-based reformulation. Here, we leverage polynomi…

math.OC2025

Separable approximations of optimal value functions under a decaying sensitivity assumption

Mario Sperl, Luca Saluzzi, Lars Grüne +1

An efficient approach for the construction of separable approximations of optimal value functions from interconnected optimal control problems is presented. The approach is based o…