Gebken, B. (2024). A note on the convergence of deterministic gradient sampling in nonsmooth optimization. Computational Optimization and Applications.https://doi.org/10.1007/s10589-024-00552-0 

Sonntag, K., Gebken, B., Müller, G., Peitz, S., &; Volkwein, S. (2024). A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. In arXiv:2402.06376.

Gebken, B., Bieker, K., &; Peitz, S. (2023). On the structure of regularization paths for piecewise differentiable regularization terms. Journal of Global Optimization, 85(3), 709–741.https://doi.org/10.1007/s10898-022-01223-2 

Bernreuther, M., Dellnitz, M., Gebken, B., Müller, G., Peitz, S., Sonntag, K., &; Volkwein, S. (2023). Multiobjective Optimization of Non-Smooth PDE-Constrained Problems. In arXiv:2308.01113.

Banholzer, S., Gebken, B., Dellnitz, M., Peitz, S., &; Volkwein, S. (2022). ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation. In H. Michael, H. Roland, K. Christian, U. Michael, &; U. Stefan (Eds.), Non-Smooth and Complementarity-Based Distributed Parameter Systems (pp. 43–76). Springer.https://doi.org/10.1007/978-3-030-79393-7_3 

Gebken, B. (2022). Using second-order information in gradient sampling methods for  nonsmooth optimization. In arXiv:2210.04579.

Gebken, B. (2022). Computation and analysis of Pareto critical sets in smooth and nonsmooth multiobjective optimization.https://doi.org/10.17619/UNIPB/1-1327 

Bieker, K., Gebken, B., &; Peitz, S. (2022). On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7797–7808.https://doi.org/10.1109/TPAMI.2021.3114962 

Gebken, B., &; Peitz, S. (2021). An efficient descent method for locally Lipschitz multiobjective optimization problems. Journal of Optimization Theory and Applications, 188, 696–723.https://doi.org/10.1007/s10957-020-01803-w 

Gebken, B., &; Peitz, S. (2021). Inverse multiobjective optimization: Inferring decision criteria from data. Journal of Global Optimization, 80, 3–29.https://doi.org/10.1007/s10898-020-00983-z 

Dellnitz, M., Gebken, B., Gerlach, R., &; Klus, S. (2020). On the equivariance properties of self-adjoint matrices. Dynamical Systems, 35(2), 197–215.https://doi.org/10.1080/14689367.2019.1661355 

Gebken, B., Peitz, S., &; Dellnitz, M. (2019). On the hierarchical structure of Pareto critical sets. Journal of Global Optimization, 73(4), 891–913.https://doi.org/10.1007/s10898-019-00737-6 

Gebken, B., Peitz, S., &; Dellnitz, M. (2018). A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems. In Numerical and Evolutionary Optimization – NEO 2017. Cham.https://doi.org/10.1007/978-3-319-96104-0_2