Using second-order information in gradient sampling methods for  nonsmooth optimization
B. Gebken, ArXiv:2210.04579 (2022).
LibreCat: /record/34618
arXiv: 2210.04579

Computation and analysis of Pareto critical sets in smooth and nonsmooth multiobjective optimization
B. Gebken, Computation and Analysis of Pareto Critical Sets in Smooth and Nonsmooth Multiobjective Optimization, 2022.
LibreCat: /record/31556
DOI: 10.17619/UNIPB/1-1327

On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation
K. Bieker, B. Gebken, S. Peitz, IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2022) 7797–7808.
LibreCat: /record/20731
DOI: 10.1109/TPAMI.2021.3114962

An efficient descent method for locally Lipschitz multiobjective optimization problems
B. Gebken, S. Peitz, Journal of Optimization Theory and Applications 188 (2021) 696–723.
LibreCat: /record/16867
DOI: 10.1007/s10957-020-01803-w

Inverse multiobjective optimization: Inferring decision criteria from data
B. Gebken, S. Peitz, Journal of Global Optimization 80 (2021) 3–29.
LibreCat: /record/16295
DOI: 10.1007/s10898-020-00983-z

On the equivariance properties of self-adjoint matrices
M. Dellnitz, B. Gebken, R. Gerlach, S. Klus, Dynamical Systems 35 (2020) 197–215.
LibreCat: /record/16712
DOI: 10.1080/14689367.2019.1661355

On the hierarchical structure of Pareto critical sets
B. Gebken, S. Peitz, M. Dellnitz, Journal of Global Optimization 73 (2019) 891–913.
LibreCat: /record/10595
DOI: 10.1007/s10898-019-00737-6

A Descent Method for Equality and Inequality Constrained Multiobjective Optimization Problems
B. Gebken, S. Peitz, M. Dellnitz, in: Numerical and Evolutionary Optimization – NEO 2017, Cham, 2018.
LibreCat: /record/8750
DOI: 10.1007/978-3-319-96104-0_2