Publications (27)
Dynamic programming for optimal stopping via pseudo-regression
Christian Bayer, Martin Redmann, John Schoenmakers
We introduce new variants of classical regression-based algorithms for optimal stopping problems based on computation of regression coefficients by Monte Carlo approximation of the…
Model reduction for stochastic systems with nonlinear drift
Martin Redmann
In this paper, we study dimension reduction techniques for large-scale controlled stochastic differential equations (SDEs). The drift of the considered SDEs contains a polynomial t…
Signature-Based Universal Bilinear Approximations for Nonlinear Systems and Model Order Reduction
Martin Redmann, Justus Werner
This paper deals with non-Lipschitz nonlinear systems. Such systems can be approximated by a linear map of so-called signatures, which play a crucial role in the theory of rough pa…
Solving high-dimensional optimal stopping problems using optimization based model order reduction
Martin Redmann
Solving optimal stopping problems by backward induction in high dimensions is often very complex since the computation of conditional expectations is required. Typically, such comp…
Bilinear systems -- A new link to -norms, relations to stochastic systems and further properties
Martin Redmann
In this paper, we prove several new results that give new insights into bilinear systems. We discuss conditions for asymptotic stability using probabilistic arguments. Moreover, we…
Dimension reduction for large-scale stochastic systems with non-zero initial states and controlled diffusion
Martin Redmann
In this paper, we establish new strategies to reduce the dimension of large-scale controlled stochastic differential equations with non-zero initial states. The first approach tran…