We have just published a preprint where we study the regularization paths of optimization problems with regularization terms. Regularization is used in many different areas of optimization when solutions are sought which not only minimize a given function, but also possess a certain degree of regularity. Popular applications are image denoising, sparse regression and machine learning. Since the choice of the regularization parameter is crucial…

Our paper "On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation" (DOI: 10.1109/TPAMI.2021.3114962) has appeared in the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) as an Open Access paper.

We have just published a preprint with the first finite-data error analysis for the Koopman operator in the stochastic and/or control setting. More details can be found at https://ris.uni-paderborn.de/publication/23428, and the paper can be downloaded from the arXiv.

Manuel Berkemeier's paper titled "Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models" has been published in the journal Mathematical and Computational Applications (MCA), 26(2), 31.

With the start of the summer term 2021, a new research group has been established in the Department of Computer Science. Jun.-Prof. Dr. Sebastian Peitz has been appointed head of the area "Data Science for Engineering". The research focus will be the development and the application of data-science and machine-learning-based methods in the context of engineering applications. The department's press release can be found here (in german).