@misc{BetzBinkowskiFischbachetal.2025, author = {Betz, Fridtjof and Binkowski, Felix and Fischbach, Jan David and Feldman, Nick and Zschiedrich, Lin and Rockstuhl, Carsten and Koenderink, A. Femius and Burger, Sven}, title = {Uncovering Hidden Resonances in Non-Hermitian Systems with Scattering Thresholds (Laser Photonics Rev. 19(21)/2025)}, volume = {19}, journal = {Laser Photonics Rev.}, number = {21}, doi = {10.1002/lpor.70534}, pages = {e70534}, year = {2025}, language = {en} } @misc{HaslerAmrheinKlindtetal.2017, author = {Hasler, Tim and Amrhein, Kilian and Klindt, Marco and Oltmanns, Elias and Peters-Kottig, Wolfgang}, title = {Backup ist KEINE Langzeitverf{\"u}gbarkeit}, doi = {10.12752/9954}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-99548}, year = {2017}, abstract = {Diese Handreichung im Flyerformat soll mit den verschiedenen Missverst{\"a}ndnissen aufr{\"a}umen, die einem im Alltag begegnen, wenn man es mit digitaler Langzeitverf{\"u}gbarkeit zu tun hat.}, language = {de} } @misc{RocheBetzYangetal.2024, author = {Roche, L{\´e}o J. and Betz, Fridtjof and Yang, Yuhui and Limame, Imad and Shih, Ching-Wen and Burger, Sven and Reitzenstein, Stephan}, title = {Inside Front Cover: Numerical Investigation of a Coupled Micropillar - Waveguide System for Integrated Quantum Photonic Circuits (Adv. Quantum Technol. 12/2024)}, volume = {7}, journal = {Adv. Quantum Technol.}, doi = {10.1002/qute.202470036}, pages = {2470036}, year = {2024}, language = {en} } @misc{VuKochXu2024, author = {Vu, Thi Huong and Koch, Thorsten and Xu, Hong-Kun}, title = {The gradient projection method: Is the Polyak adaptive stepsize rule optimal?}, year = {2024}, abstract = {Not always! This is our answer to the question of whether the Polyak adaptive stepsize rule in the gradient projection method is optimal. The answer is based on revisiting the subgradient projection method by Polyak [USSR Computational Mathematics and Mathematical Physics 9 (1969)] for smooth and convex minimization problems where the objective function possesses a geometric property called flatness. Our results show that the method can be more flexible (the effective range for the parameter controlling the stepsize can be wider) and have sharper convergence rates. Applications to split feasibility/equality problems are presented, deriving for the first time the O(1/k) rate of convergence for the adaptive CQ method. A theoretical guarantee of the linear convergence of the gradient descent method with adaptive stepsizes for Google PageRank is provided. At the same time, numerical experiments are designed to spot the ``optimal" stepsize and to compare with other basic gradient methods.}, language = {en} }