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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…

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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.

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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.

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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.

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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).

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