--- _id: '57472' abstract: - lang: eng text: In this paper we introduce, in a Hilbert space setting, a second order dynamical system with asymptotically vanishing damping and vanishing Tikhonov regularization that approaches a multiobjective optimization problem with convex and differentiable components of the objective function. Trajectory solutions are shown to exist in finite dimensions. We prove fast convergence of the function values, quantified in terms of a merit function. Based on the regime considered, we establish both weak and, in some cases, strong convergence of trajectory solutions toward a weak Pareto optimal solution. To achieve this, we apply Tikhonov regularization individually to each component of the objective function. This work extends results from single objective convex optimization into the multiobjective setting. author: - first_name: Radu Ioan full_name: Bot, Radu Ioan last_name: Bot - first_name: Konstantin full_name: Sonntag, Konstantin id: '56399' last_name: Sonntag orcid: https://orcid.org/0000-0003-3384-3496 citation: ama: Bot RI, Sonntag K. Inertial dynamics with vanishing Tikhonov regularization for multobjective optimization. Journal of Mathematical Analysis and Applications. Published online 2025. apa: Bot, R. I., & Sonntag, K. (2025). Inertial dynamics with vanishing Tikhonov regularization for multobjective optimization. Journal of Mathematical Analysis and Applications. bibtex: '@article{Bot_Sonntag_2025, title={Inertial dynamics with vanishing Tikhonov regularization for multobjective optimization}, journal={Journal of Mathematical Analysis and Applications}, author={Bot, Radu Ioan and Sonntag, Konstantin}, year={2025} }' chicago: Bot, Radu Ioan, and Konstantin Sonntag. “Inertial Dynamics with Vanishing Tikhonov Regularization for Multobjective Optimization.” Journal of Mathematical Analysis and Applications, 2025. ieee: R. I. Bot and K. Sonntag, “Inertial dynamics with vanishing Tikhonov regularization for multobjective optimization,” Journal of Mathematical Analysis and Applications, 2025. mla: Bot, Radu Ioan, and Konstantin Sonntag. “Inertial Dynamics with Vanishing Tikhonov Regularization for Multobjective Optimization.” Journal of Mathematical Analysis and Applications, 2025. short: R.I. Bot, K. Sonntag, Journal of Mathematical Analysis and Applications (2025). date_created: 2024-11-28T08:58:17Z date_updated: 2025-10-16T11:56:36Z ddc: - '510' department: - _id: '101' - _id: '530' - _id: '655' external_id: arxiv: - '2411.18422' file: - access_level: open_access content_type: application/pdf creator: sonntagk date_created: 2024-11-28T08:58:00Z date_updated: 2024-11-28T08:58:00Z file_id: '57473' file_name: Inertial dynamics with vanishing Tikhonov regularization for multobjective optimization.pdf file_size: 4291134 relation: main_file file_date_updated: 2024-11-28T08:58:00Z has_accepted_license: '1' keyword: - Pareto optimization - Lyapunov analysis - gradient-like dynamical systems - inertial dynamics - asymptotic vanishing damping - Tikhonov regularization - strong convergence language: - iso: eng main_file_link: - url: https://arxiv.org/pdf/2411.18422 oa: '1' publication: Journal of Mathematical Analysis and Applications status: public title: Inertial dynamics with vanishing Tikhonov regularization for multobjective optimization type: journal_article user_id: '56399' year: '2025' ... --- _id: '62750' abstract: - lang: eng text: 'Diese Dissertation enthält Beiträge zum Bereich der Mehrzieloptimierung mit einem Fokus auf unbeschränkten Problemen, die auf einem allgemeinen Hilbertraum definiert sind. Für Mehrzieloptimierungsprobleme mit lokal Lipschitz-stetigen Zielfunktionen definieren wir ein multikriterielles Subdifferential, das wir erstmals im Kontext allgemeiner Hilberträume analysieren. Aufbauend auf diesen theoretischen Untersuchungen präsentieren wir ein Abstiegsverfahren, bei welchem in jeder Iteration eine Abstiegsrichtung mittels einer numerischen Approximation des multikriteriellen Subdifferentials bestimmt wird. Im Kontext konvexer, stetig differenzierbarer Zielfunktionen mit Lipschitz-stetigen Gradienten, führen wir eine Familie von dynamischen Gradientensystemen mit Trägheitsterm ein, die bekannte kontinuierliche Systeme aus der skalaren Optimierung verallgemeinern. Wir stellen drei neue Systeme vor: eines mit konstanter Dämpfung, eines mit asymptotisch abnehmender Dämpfung und eines, das zusätzlich eine zeitabhängige Tikhonov-Regularisierung beinhaltet. Aufbauend auf den Untersuchungen der neuen dynamischen Gradientensysteme, entwickeln wir ein beschleunigtes Gradientenverfahren zur Mehrzieloptimierung, das auf einer Diskretisierung des multikriteriellen Gradientensystems mit asymptotisch abnehmender Dämpfung beruht. Das hergeleitete Verfahren bewahrt die günstigen Konvergenzeigenschaften des kontinuierlichen Systems und erreicht eine schnellere Konvergenz als klassische Verfahren.' - lang: eng text: 'This dissertation contributes to the field of multiobjective optimization, with a focus on unconstrained problems formulated in a general Hilbert space. For multiobjective optimization problems with locally Lipschitz continuous objective functions, we define a multiobjective subdifferential, which we analyze for the first time in the context of general Hilbert spaces. Building on these theoretical investigations, we present a descent method in which, at each iteration, a descent direction is determined via a numerical approximation of the multiobjective subdifferential. In the setting of convex, continuously differentiable objective functions with Lipschitz continuous gradients, we introduce a family of inertial gradient dynamical systems that generalize well-known continuous-time systems from scalar optimization. We present three novel systems: one with constant damping, one with asymptotic vanishing damping, and one combining vanishing damping with time-dependent Tikhonov regularization. Building on the investigation of the novel gradient dynamical systems, we develop an accelerated gradient method for multiobjective optimization via discretization of the multiobjective gradient system with asymptotic vanishing damping. The proposed method retains the favorable convergence properties of the continuous system while achieving faster convergence than standard approaches, such as classical methods.' author: - first_name: Konstantin full_name: Sonntag, Konstantin id: '56399' last_name: Sonntag orcid: https://orcid.org/0000-0003-3384-3496 citation: ama: Sonntag K. First-Order Methods and Gradient Dynamical Systems for Multiobjective Optimization. Paderborn University; 2025. doi:10.17619/UNIPB/1-2457 apa: Sonntag, K. (2025). First-order methods and gradient dynamical systems for multiobjective optimization. Paderborn University. https://doi.org/10.17619/UNIPB/1-2457 bibtex: '@book{Sonntag_2025, title={First-order methods and gradient dynamical systems for multiobjective optimization}, DOI={10.17619/UNIPB/1-2457}, publisher={Paderborn University}, author={Sonntag, Konstantin}, year={2025} }' chicago: Sonntag, Konstantin. First-Order Methods and Gradient Dynamical Systems for Multiobjective Optimization. Paderborn University, 2025. https://doi.org/10.17619/UNIPB/1-2457. ieee: K. Sonntag, First-order methods and gradient dynamical systems for multiobjective optimization. Paderborn University, 2025. mla: Sonntag, Konstantin. First-Order Methods and Gradient Dynamical Systems for Multiobjective Optimization. Paderborn University, 2025, doi:10.17619/UNIPB/1-2457. short: K. Sonntag, First-Order Methods and Gradient Dynamical Systems for Multiobjective Optimization, Paderborn University, 2025. date_created: 2025-12-03T06:55:01Z date_updated: 2025-12-03T07:04:36Z ddc: - '510' department: - _id: '101' - _id: '530' doi: 10.17619/UNIPB/1-2457 has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://digital.ub.uni-paderborn.de/hs/download/pdf/8141881 oa: '1' publisher: Paderborn University status: public supervisor: - first_name: Michael full_name: Dellnitz, Michael last_name: Dellnitz - first_name: Sina full_name: Ober-Blöbaum, Sina id: '16494' last_name: Ober-Blöbaum title: First-order methods and gradient dynamical systems for multiobjective optimization type: dissertation user_id: '56399' year: '2025' ... --- _id: '46019' abstract: - lang: eng text: We derive efficient algorithms to compute weakly Pareto optimal solutions for smooth, convex and unconstrained multiobjective optimization problems in general Hilbert spaces. To this end, we define a novel inertial gradient-like dynamical system in the multiobjective setting, which trajectories converge weakly to Pareto optimal solutions. Discretization of this system yields an inertial multiobjective algorithm which generates sequences that converge weakly to Pareto optimal solutions. We employ Nesterov acceleration to define an algorithm with an improved convergence rate compared to the plain multiobjective steepest descent method (Algorithm 1). A further improvement in terms of efficiency is achieved by avoiding the solution of a quadratic subproblem to compute a common step direction for all objective functions, which is usually required in first-order methods. Using a different discretization of our inertial gradient-like dynamical system, we obtain an accelerated multiobjective gradient method that does not require the solution of a subproblem in each step (Algorithm 2). While this algorithm does not converge in general, it yields good results on test problems while being faster than standard steepest descent. author: - first_name: Konstantin full_name: Sonntag, Konstantin id: '56399' last_name: Sonntag orcid: https://orcid.org/0000-0003-3384-3496 - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X citation: ama: Sonntag K, Peitz S. Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. Journal of Optimization Theory and Applications. Published online 2024. doi:10.1007/s10957-024-02389-3 apa: Sonntag, K., & Peitz, S. (2024). Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems. Journal of Optimization Theory and Applications. https://doi.org/10.1007/s10957-024-02389-3 bibtex: '@article{Sonntag_Peitz_2024, title={Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems}, DOI={10.1007/s10957-024-02389-3}, journal={Journal of Optimization Theory and Applications}, publisher={Springer}, author={Sonntag, Konstantin and Peitz, Sebastian}, year={2024} }' chicago: Sonntag, Konstantin, and Sebastian Peitz. “Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems.” Journal of Optimization Theory and Applications, 2024. https://doi.org/10.1007/s10957-024-02389-3. ieee: 'K. Sonntag and S. Peitz, “Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems,” Journal of Optimization Theory and Applications, 2024, doi: 10.1007/s10957-024-02389-3.' mla: Sonntag, Konstantin, and Sebastian Peitz. “Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems.” Journal of Optimization Theory and Applications, Springer, 2024, doi:10.1007/s10957-024-02389-3. short: K. Sonntag, S. Peitz, Journal of Optimization Theory and Applications (2024). date_created: 2023-07-12T06:35:58Z date_updated: 2024-02-21T10:13:33Z department: - _id: '101' - _id: '655' doi: 10.1007/s10957-024-02389-3 language: - iso: eng main_file_link: - open_access: '1' url: https://link.springer.com/content/pdf/10.1007/s10957-024-02389-3.pdf oa: '1' publication: Journal of Optimization Theory and Applications publication_status: published publisher: Springer status: public title: Fast Multiobjective Gradient Methods with Nesterov Acceleration via Inertial Gradient-Like Systems type: journal_article user_id: '56399' year: '2024' ... --- _id: '51334' abstract: - lang: eng text: The efficient optimization method for locally Lipschitz continuous multiobjective optimization problems from [1] is extended from finite-dimensional problems to general Hilbert spaces. The method iteratively computes Pareto critical points, where in each iteration, an approximation of the subdifferential is computed in an efficient manner and then used to compute a common descent direction for all objective functions. To prove convergence, we present some new optimality results for nonsmooth multiobjective optimization problems in Hilbert spaces. Using these, we can show that every accumulation point of the sequence generated by our algorithm is Pareto critical under common assumptions. Computational efficiency for finding Pareto critical points is numerically demonstrated for multiobjective optimal control of an obstacle problem. author: - first_name: Konstantin full_name: Sonntag, Konstantin id: '56399' last_name: Sonntag orcid: https://orcid.org/0000-0003-3384-3496 - first_name: Bennet full_name: Gebken, Bennet id: '32643' last_name: Gebken - first_name: Georg full_name: Müller, Georg last_name: Müller - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X - first_name: Stefan full_name: Volkwein, Stefan last_name: Volkwein citation: ama: Sonntag K, Gebken B, Müller G, Peitz S, Volkwein S. A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces. arXiv:240206376. Published online 2024. apa: 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. bibtex: '@article{Sonntag_Gebken_Müller_Peitz_Volkwein_2024, title={A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces}, journal={arXiv:2402.06376}, author={Sonntag, Konstantin and Gebken, Bennet and Müller, Georg and Peitz, Sebastian and Volkwein, Stefan}, year={2024} }' chicago: Sonntag, Konstantin, Bennet Gebken, Georg Müller, Sebastian Peitz, and Stefan Volkwein. “A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces.” ArXiv:2402.06376, 2024. ieee: K. Sonntag, B. Gebken, G. Müller, S. Peitz, and S. Volkwein, “A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces,” arXiv:2402.06376. 2024. mla: Sonntag, Konstantin, et al. “A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces.” ArXiv:2402.06376, 2024. short: K. Sonntag, B. Gebken, G. Müller, S. Peitz, S. Volkwein, ArXiv:2402.06376 (2024). date_created: 2024-02-13T09:35:26Z date_updated: 2024-02-21T10:21:03Z department: - _id: '101' - _id: '655' external_id: arxiv: - "\t2402.06376" has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2402.06376 oa: '1' publication: arXiv:2402.06376 status: public title: A Descent Method for Nonsmooth Multiobjective Optimization in Hilbert Spaces type: preprint user_id: '56399' year: '2024' ... --- _id: '53858' author: - first_name: Junaid full_name: Akhter, Junaid id: '97994' last_name: Akhter - first_name: Paul David full_name: Fährmann, Paul David last_name: Fährmann - first_name: Konstantin full_name: Sonntag, Konstantin id: '56399' last_name: Sonntag orcid: https://orcid.org/0000-0003-3384-3496 - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X citation: ama: Akhter J, Fährmann PD, Sonntag K, Peitz S. Common pitfalls to avoid while using multiobjective optimization in machine learning. arXiv. Published online 2024. apa: Akhter, J., Fährmann, P. D., Sonntag, K., & Peitz, S. (2024). Common pitfalls to avoid while using multiobjective optimization in machine learning. In arXiv. bibtex: '@article{Akhter_Fährmann_Sonntag_Peitz_2024, title={Common pitfalls to avoid while using multiobjective optimization in machine learning}, journal={arXiv}, author={Akhter, Junaid and Fährmann, Paul David and Sonntag, Konstantin and Peitz, Sebastian}, year={2024} }' chicago: Akhter, Junaid, Paul David Fährmann, Konstantin Sonntag, and Sebastian Peitz. “Common Pitfalls to Avoid While Using Multiobjective Optimization in Machine Learning.” ArXiv, 2024. ieee: J. Akhter, P. D. Fährmann, K. Sonntag, and S. Peitz, “Common pitfalls to avoid while using multiobjective optimization in machine learning,” arXiv. 2024. mla: Akhter, Junaid, et al. “Common Pitfalls to Avoid While Using Multiobjective Optimization in Machine Learning.” ArXiv, 2024. short: J. Akhter, P.D. Fährmann, K. Sonntag, S. Peitz, ArXiv (2024). date_created: 2024-05-03T13:38:34Z date_updated: 2024-05-06T08:29:38Z department: - _id: '655' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/pdf/2405.01480 oa: '1' publication: arXiv status: public title: Common pitfalls to avoid while using multiobjective optimization in machine learning type: preprint user_id: '47427' year: '2024' ... --- _id: '32447' abstract: - lang: eng text: 'We present a new gradient-like dynamical system related to unconstrained convex smooth multiobjective optimization which involves inertial effects and asymptotic vanishing damping. To the best of our knowledge, this system is the first inertial gradient-like system for multiobjective optimization problems including asymptotic vanishing damping, expanding the ideas previously laid out in [H. Attouch and G. Garrigos, Multiobjective Optimization: An Inertial Dynamical Approach to Pareto Optima, preprint, arXiv:1506.02823, 2015]. We prove existence of solutions to this system in finite dimensions and further prove that its bounded solutions converge weakly to weakly Pareto optimal points. In addition, we obtain a convergence rate of order \(\mathcal{O}(t^{-2})\) for the function values measured with a merit function. This approach presents a good basis for the development of fast gradient methods for multiobjective optimization.' article_type: original author: - first_name: Konstantin full_name: Sonntag, Konstantin id: '56399' last_name: Sonntag orcid: https://orcid.org/0000-0003-3384-3496 - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X citation: ama: Sonntag K, Peitz S. Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping. SIAM Journal on Optimization. 2024;34(3):2259-2286. doi:10.1137/23M1588512 apa: Sonntag, K., & Peitz, S. (2024). Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping. SIAM Journal on Optimization, 34(3), 2259–2286. https://doi.org/10.1137/23M1588512 bibtex: '@article{Sonntag_Peitz_2024, title={Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping}, volume={34}, DOI={10.1137/23M1588512}, number={3}, journal={SIAM Journal on Optimization}, publisher={Society for Industrial and Applied Mathematics}, author={Sonntag, Konstantin and Peitz, Sebastian}, year={2024}, pages={2259–2286} }' chicago: 'Sonntag, Konstantin, and Sebastian Peitz. “Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping.” SIAM Journal on Optimization 34, no. 3 (2024): 2259–86. https://doi.org/10.1137/23M1588512.' ieee: 'K. Sonntag and S. Peitz, “Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping,” SIAM Journal on Optimization, vol. 34, no. 3, pp. 2259–2286, 2024, doi: 10.1137/23M1588512.' mla: Sonntag, Konstantin, and Sebastian Peitz. “Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping.” SIAM Journal on Optimization, vol. 34, no. 3, Society for Industrial and Applied Mathematics, 2024, pp. 2259–86, doi:10.1137/23M1588512. short: K. Sonntag, S. Peitz, SIAM Journal on Optimization 34 (2024) 2259–2286. date_created: 2022-07-28T11:53:02Z date_updated: 2024-07-02T09:27:39Z department: - _id: '101' - _id: '655' doi: 10.1137/23M1588512 intvolume: ' 34' issue: '3' keyword: - multiobjective optimization - Pareto optimization - Lyapunov analysis - gradient-likedynamical systems - inertial dynamics - asymptotic vanishing damping - fast convergence language: - iso: eng page: 2259 - 2286 publication: SIAM Journal on Optimization publication_identifier: issn: - 1095-7189 publication_status: published publisher: Society for Industrial and Applied Mathematics status: public title: Fast Convergence of Inertial Multiobjective Gradient-Like Systems with Asymptotic Vanishing Damping type: journal_article user_id: '56399' volume: 34 year: '2024' ... --- _id: '51159' abstract: - lang: eng text: Sparsity is a highly desired feature in deep neural networks (DNNs) since it ensures numerical efficiency, improves the interpretability of models (due to the smaller number of relevant features), and robustness. In machine learning approaches based on linear models, it is well known that there exists a connecting path between the sparsest solution in terms of the $\ell^1$ norm,i.e., zero weights and the non-regularized solution, which is called the regularization path. Very recently, there was a first attempt to extend the concept of regularization paths to DNNs by means of treating the empirical loss and sparsity ($\ell^1$ norm) as two conflicting criteria and solving the resulting multiobjective optimization problem. However, due to the non-smoothness of the $\ell^1$ norm and the high number of parameters, this approach is not very efficient from a computational perspective. To overcome this limitation, we present an algorithm that allows for the approximation of the entire Pareto front for the above-mentioned objectives in a very efficient manner. We present numerical examples using both deterministic and stochastic gradients. We furthermore demonstrate that knowledge of the regularization path allows for a well-generalizing network parametrization. author: - first_name: Augustina Chidinma full_name: Amakor, Augustina Chidinma id: '97916' last_name: Amakor - first_name: Konstantin full_name: Sonntag, Konstantin id: '56399' last_name: Sonntag - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X citation: ama: Amakor AC, Sonntag K, Peitz S. A multiobjective continuation method to compute the regularization path of deep neural networks. arXiv. Published online 2023. apa: Amakor, A. C., Sonntag, K., & Peitz, S. (2023). A multiobjective continuation method to compute the regularization path of deep neural networks. In arXiv. bibtex: '@article{Amakor_Sonntag_Peitz_2023, title={A multiobjective continuation method to compute the regularization path of deep neural networks}, journal={arXiv}, author={Amakor, Augustina Chidinma and Sonntag, Konstantin and Peitz, Sebastian}, year={2023} }' chicago: Amakor, Augustina Chidinma, Konstantin Sonntag, and Sebastian Peitz. “A Multiobjective Continuation Method to Compute the Regularization Path of Deep Neural Networks.” ArXiv, 2023. ieee: A. C. Amakor, K. Sonntag, and S. Peitz, “A multiobjective continuation method to compute the regularization path of deep neural networks,” arXiv. 2023. mla: Amakor, Augustina Chidinma, et al. “A Multiobjective Continuation Method to Compute the Regularization Path of Deep Neural Networks.” ArXiv, 2023. short: A.C. Amakor, K. Sonntag, S. Peitz, ArXiv (2023). date_created: 2024-02-06T08:51:00Z date_updated: 2024-02-06T08:52:07Z department: - _id: '655' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/pdf/2308.12044.pdf oa: '1' publication: arXiv status: public title: A multiobjective continuation method to compute the regularization path of deep neural networks type: preprint user_id: '47427' year: '2023' ... --- _id: '46578' abstract: - lang: eng text: 'Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control - potentially with non-smoothness both on the level of the objectives or in the system dynamics. This results in new challenges such as dealing with expensive models (e.g., governed by partial differential equations (PDEs)) and developing dedicated algorithms handling the non-smoothness. Since in contrast to single-objective optimization, the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in the field of multiobjective optimization of non-smooth PDE-constrained problems. In particular we report on the advances achieved within Project 2 "Multiobjective Optimization of Non-Smooth PDE-Constrained Problems - Switches, State Constraints and Model Order Reduction" of the DFG Priority Programm 1962 "Non-smooth and Complementarity-based Distributed Parameter Systems: Simulation and Hierarchical Optimization".' author: - first_name: Marco full_name: Bernreuther, Marco last_name: Bernreuther - first_name: Michael full_name: Dellnitz, Michael last_name: Dellnitz - first_name: Bennet full_name: Gebken, Bennet id: '32643' last_name: Gebken - first_name: Georg full_name: Müller, Georg last_name: Müller - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X - first_name: Konstantin full_name: Sonntag, Konstantin id: '56399' last_name: Sonntag orcid: https://orcid.org/0000-0003-3384-3496 - first_name: Stefan full_name: Volkwein, Stefan last_name: Volkwein citation: ama: Bernreuther M, Dellnitz M, Gebken B, et al. Multiobjective Optimization of Non-Smooth PDE-Constrained Problems. arXiv:230801113. Published online 2023. apa: 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. bibtex: '@article{Bernreuther_Dellnitz_Gebken_Müller_Peitz_Sonntag_Volkwein_2023, title={Multiobjective Optimization of Non-Smooth PDE-Constrained Problems}, journal={arXiv:2308.01113}, author={Bernreuther, Marco and Dellnitz, Michael and Gebken, Bennet and Müller, Georg and Peitz, Sebastian and Sonntag, Konstantin and Volkwein, Stefan}, year={2023} }' chicago: Bernreuther, Marco, Michael Dellnitz, Bennet Gebken, Georg Müller, Sebastian Peitz, Konstantin Sonntag, and Stefan Volkwein. “Multiobjective Optimization of Non-Smooth PDE-Constrained Problems.” ArXiv:2308.01113, 2023. ieee: M. Bernreuther et al., “Multiobjective Optimization of Non-Smooth PDE-Constrained Problems,” arXiv:2308.01113. 2023. mla: Bernreuther, Marco, et al. “Multiobjective Optimization of Non-Smooth PDE-Constrained Problems.” ArXiv:2308.01113, 2023. short: M. Bernreuther, M. Dellnitz, B. Gebken, G. Müller, S. Peitz, K. Sonntag, S. Volkwein, ArXiv:2308.01113 (2023). date_created: 2023-08-21T05:50:12Z date_updated: 2024-02-21T12:22:20Z department: - _id: '655' - _id: '101' external_id: arxiv: - '2308.01113' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/pdf/2308.01113 oa: '1' publication: arXiv:2308.01113 status: public title: Multiobjective Optimization of Non-Smooth PDE-Constrained Problems type: preprint user_id: '47427' year: '2023' ...