---
_id: '51208'
abstract:
- lang: eng
text: AbstractApproximation of subdifferentials
is one of the main tasks when computing descent directions for nonsmooth optimization
problems. In this article, we propose a bisection method for weakly lower semismooth
functions which is able to compute new subgradients that improve a given approximation
in case a direction with insufficient descent was computed. Combined with a recently
proposed deterministic gradient sampling approach, this yields a deterministic
and provably convergent way to approximate subdifferentials for computing descent
directions.
author:
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
citation:
ama: Gebken B. A note on the convergence of deterministic gradient sampling in nonsmooth
optimization. Computational Optimization and Applications. Published online
2024. doi:10.1007/s10589-024-00552-0
apa: 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
bibtex: '@article{Gebken_2024, title={A note on the convergence of deterministic
gradient sampling in nonsmooth optimization}, DOI={10.1007/s10589-024-00552-0},
journal={Computational Optimization and Applications}, publisher={Springer Science
and Business Media LLC}, author={Gebken, Bennet}, year={2024} }'
chicago: Gebken, Bennet. “A Note on the Convergence of Deterministic Gradient Sampling
in Nonsmooth Optimization.” Computational Optimization and Applications,
2024. https://doi.org/10.1007/s10589-024-00552-0.
ieee: 'B. Gebken, “A note on the convergence of deterministic gradient sampling
in nonsmooth optimization,” Computational Optimization and Applications,
2024, doi: 10.1007/s10589-024-00552-0.'
mla: Gebken, Bennet. “A Note on the Convergence of Deterministic Gradient Sampling
in Nonsmooth Optimization.” Computational Optimization and Applications,
Springer Science and Business Media LLC, 2024, doi:10.1007/s10589-024-00552-0.
short: B. Gebken, Computational Optimization and Applications (2024).
date_created: 2024-02-07T07:23:23Z
date_updated: 2024-02-08T08:05:54Z
department:
- _id: '101'
doi: 10.1007/s10589-024-00552-0
keyword:
- Applied Mathematics
- Computational Mathematics
- Control and Optimization
language:
- iso: eng
publication: Computational Optimization and Applications
publication_identifier:
issn:
- 0926-6003
- 1573-2894
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: A note on the convergence of deterministic gradient sampling in nonsmooth optimization
type: journal_article
user_id: '32643'
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: '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'
...
---
_id: '27426'
abstract:
- lang: eng
text: "Regularization is used in many different areas of optimization when solutions\r\nare
sought which not only minimize a given function, but also possess a certain\r\ndegree
of regularity. Popular applications are image denoising, sparse\r\nregression
and machine learning. Since the choice of the regularization\r\nparameter is crucial
but often difficult, path-following methods are used to\r\napproximate the entire
regularization path, i.e., the set of all possible\r\nsolutions for all regularization
parameters. Due to their nature, the\r\ndevelopment of these methods requires
structural results about the\r\nregularization path. The goal of this article
is to derive these results for\r\nthe case of a smooth objective function which
is penalized by a piecewise\r\ndifferentiable regularization term. We do this
by treating regularization as a\r\nmultiobjective optimization problem. Our results
suggest that even in this\r\ngeneral case, the regularization path is piecewise
smooth. Moreover, our theory\r\nallows for a classification of the nonsmooth features
that occur in between\r\nsmooth parts. This is demonstrated in two applications,
namely support-vector\r\nmachines and exact penalty methods."
author:
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Katharina
full_name: Bieker, Katharina
id: '32829'
last_name: Bieker
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: Gebken B, Bieker K, Peitz S. On the structure of regularization paths for piecewise
differentiable regularization terms. Journal of Global Optimization. 2023;85(3):709-741.
doi:10.1007/s10898-022-01223-2
apa: 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
bibtex: '@article{Gebken_Bieker_Peitz_2023, title={On the structure of regularization
paths for piecewise differentiable regularization terms}, volume={85}, DOI={10.1007/s10898-022-01223-2},
number={3}, journal={Journal of Global Optimization}, author={Gebken, Bennet and
Bieker, Katharina and Peitz, Sebastian}, year={2023}, pages={709–741} }'
chicago: 'Gebken, Bennet, Katharina Bieker, and Sebastian Peitz. “On the Structure
of Regularization Paths for Piecewise Differentiable Regularization Terms.” Journal
of Global Optimization 85, no. 3 (2023): 709–41. https://doi.org/10.1007/s10898-022-01223-2.'
ieee: 'B. Gebken, K. Bieker, and S. Peitz, “On the structure of regularization paths
for piecewise differentiable regularization terms,” Journal of Global Optimization,
vol. 85, no. 3, pp. 709–741, 2023, doi: 10.1007/s10898-022-01223-2.'
mla: Gebken, Bennet, et al. “On the Structure of Regularization Paths for Piecewise
Differentiable Regularization Terms.” Journal of Global Optimization, vol.
85, no. 3, 2023, pp. 709–41, doi:10.1007/s10898-022-01223-2.
short: B. Gebken, K. Bieker, S. Peitz, Journal of Global Optimization 85 (2023)
709–741.
date_created: 2021-11-15T09:24:59Z
date_updated: 2023-03-11T17:16:33Z
department:
- _id: '101'
- _id: '655'
doi: 10.1007/s10898-022-01223-2
intvolume: ' 85'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/content/pdf/10.1007/s10898-022-01223-2.pdf
oa: '1'
page: 709-741
publication: Journal of Global Optimization
status: public
title: On the structure of regularization paths for piecewise differentiable regularization
terms
type: journal_article
user_id: '47427'
volume: 85
year: '2023'
...
---
_id: '16296'
abstract:
- lang: eng
text: "Multiobjective optimization plays an increasingly important role in modern\r\napplications,
where several objectives are often of equal importance. The task\r\nin multiobjective
optimization and multiobjective optimal control is therefore\r\nto compute the
set of optimal compromises (the Pareto set) between the\r\nconflicting objectives.
Since the Pareto set generally consists of an infinite\r\nnumber of solutions,
the computational effort can quickly become challenging\r\nwhich is particularly
problematic when the objectives are costly to evaluate as\r\nis the case for models
governed by partial differential equations (PDEs). To\r\ndecrease the numerical
effort to an affordable amount, surrogate models can be\r\nused to replace the
expensive PDE evaluations. Existing multiobjective\r\noptimization methods using
model reduction are limited either to low parameter\r\ndimensions or to few (ideally
two) objectives. In this article, we present a\r\ncombination of the reduced basis
model reduction method with a continuation\r\napproach using inexact gradients.
The resulting approach can handle an\r\narbitrary number of objectives while yielding
a significant reduction in\r\ncomputing time."
author:
- first_name: Stefan
full_name: Banholzer, Stefan
last_name: Banholzer
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: https://orcid.org/0000-0002-3389-793X
- first_name: Stefan
full_name: Volkwein, Stefan
last_name: Volkwein
citation:
ama: 'Banholzer S, Gebken B, Dellnitz M, Peitz S, Volkwein S. ROM-Based Multiobjective
Optimization of Elliptic PDEs via Numerical Continuation. In: Michael H, Roland
H, Christian K, Michael U, Stefan U, eds. Non-Smooth and Complementarity-Based
Distributed Parameter Systems. Springer; 2022:43-76. doi:10.1007/978-3-030-79393-7_3'
apa: 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
bibtex: '@inbook{Banholzer_Gebken_Dellnitz_Peitz_Volkwein_2022, place={Cham}, title={ROM-Based
Multiobjective Optimization of Elliptic PDEs via Numerical Continuation}, DOI={10.1007/978-3-030-79393-7_3},
booktitle={Non-Smooth and Complementarity-Based Distributed Parameter Systems},
publisher={Springer}, author={Banholzer, Stefan and Gebken, Bennet and Dellnitz,
Michael and Peitz, Sebastian and Volkwein, Stefan}, editor={Michael, Hintermüller
and Roland, Herzog and Christian, Kanzow and Michael, Ulbrich and Stefan, Ulbrich},
year={2022}, pages={43–76} }'
chicago: 'Banholzer, Stefan, Bennet Gebken, Michael Dellnitz, Sebastian Peitz, and
Stefan Volkwein. “ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical
Continuation.” In Non-Smooth and Complementarity-Based Distributed Parameter
Systems, edited by Hintermüller Michael, Herzog Roland, Kanzow Christian,
Ulbrich Michael, and Ulbrich Stefan, 43–76. Cham: Springer, 2022. https://doi.org/10.1007/978-3-030-79393-7_3.'
ieee: 'S. Banholzer, B. Gebken, M. Dellnitz, S. Peitz, and S. Volkwein, “ROM-Based
Multiobjective Optimization of Elliptic PDEs via Numerical Continuation,” in Non-Smooth
and Complementarity-Based Distributed Parameter Systems, H. Michael, H. Roland,
K. Christian, U. Michael, and U. Stefan, Eds. Cham: Springer, 2022, pp. 43–76.'
mla: Banholzer, Stefan, et al. “ROM-Based Multiobjective Optimization of Elliptic
PDEs via Numerical Continuation.” Non-Smooth and Complementarity-Based Distributed
Parameter Systems, edited by Hintermüller Michael et al., Springer, 2022,
pp. 43–76, doi:10.1007/978-3-030-79393-7_3.
short: 'S. Banholzer, B. Gebken, M. Dellnitz, S. Peitz, S. Volkwein, in: H. Michael,
H. Roland, K. Christian, U. Michael, U. Stefan (Eds.), Non-Smooth and Complementarity-Based
Distributed Parameter Systems, Springer, Cham, 2022, pp. 43–76.'
date_created: 2020-03-13T12:45:31Z
date_updated: 2022-03-14T13:04:51Z
department:
- _id: '101'
- _id: '655'
doi: 10.1007/978-3-030-79393-7_3
editor:
- first_name: Hintermüller
full_name: Michael, Hintermüller
last_name: Michael
- first_name: Herzog
full_name: Roland, Herzog
last_name: Roland
- first_name: Kanzow
full_name: Christian, Kanzow
last_name: Christian
- first_name: Ulbrich
full_name: Michael, Ulbrich
last_name: Michael
- first_name: Ulbrich
full_name: Stefan, Ulbrich
last_name: Stefan
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1906.09075.pdf
oa: '1'
page: 43-76
place: Cham
publication: Non-Smooth and Complementarity-Based Distributed Parameter Systems
publication_identifier:
isbn:
- 978-3-030-79392-0
publisher: Springer
status: public
title: ROM-Based Multiobjective Optimization of Elliptic PDEs via Numerical Continuation
type: book_chapter
user_id: '47427'
year: '2022'
...
---
_id: '34618'
abstract:
- lang: eng
text: "In this article, we show how second-order derivative information can be\r\nincorporated
into gradient sampling methods for nonsmooth optimization. The\r\nsecond-order
information we consider is essentially the set of coefficients of\r\nall second-order
Taylor expansions of the objective in a closed ball around a\r\ngiven point. Based
on this concept, we define a model of the objective as the\r\nmaximum of these
Taylor expansions. Iteratively minimizing this model\r\n(constrained to the closed
ball) results in a simple descent method, for which\r\nwe prove convergence to
minimal points in case the objective is convex. To\r\nobtain an implementable
method, we construct an approximation scheme for the\r\nsecond-order information
based on sampling objective values, gradients and\r\nHessian matrices at finitely
many points. Using a set of test problems, we\r\ncompare the resulting method
to five other available solvers. Considering the\r\nnumber of function evaluations,
the results suggest that the method we propose\r\nis superior to the standard
gradient sampling method, and competitive compared\r\nto other methods."
author:
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
citation:
ama: Gebken B. Using second-order information in gradient sampling methods for
nonsmooth optimization. arXiv:221004579. Published online 2022.
apa: Gebken, B. (2022). Using second-order information in gradient sampling methods
for nonsmooth optimization. In arXiv:2210.04579.
bibtex: '@article{Gebken_2022, title={Using second-order information in gradient
sampling methods for nonsmooth optimization}, journal={arXiv:2210.04579}, author={Gebken,
Bennet}, year={2022} }'
chicago: Gebken, Bennet. “Using Second-Order Information in Gradient Sampling Methods
for Nonsmooth Optimization.” ArXiv:2210.04579, 2022.
ieee: B. Gebken, “Using second-order information in gradient sampling methods for
nonsmooth optimization,” arXiv:2210.04579. 2022.
mla: Gebken, Bennet. “Using Second-Order Information in Gradient Sampling Methods
for Nonsmooth Optimization.” ArXiv:2210.04579, 2022.
short: B. Gebken, ArXiv:2210.04579 (2022).
date_created: 2022-12-20T15:25:17Z
date_updated: 2022-12-20T15:28:54Z
department:
- _id: '101'
external_id:
arxiv:
- '2210.04579'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/2210.04579
oa: '1'
publication: arXiv:2210.04579
status: public
title: Using second-order information in gradient sampling methods for nonsmooth
optimization
type: preprint
user_id: '32643'
year: '2022'
...
---
_id: '31556'
abstract:
- lang: ger
text: Mehrzieloptimierung behandelt Probleme, bei denen mehrere skalare Zielfunktionen
simultan optimiert werden sollen. Ein Punkt ist in diesem Fall optimal, wenn es
keinen anderen Punkt gibt, der mindestens genauso gut ist in allen Zielfunktionen
und besser in mindestens einer Zielfunktion. Ein notwendiges Optimalitätskriterium
lässt sich über Ableitungsinformationen erster Ordnung der Zielfunktionen herleiten.
Die Menge der Punkte, die dieses notwendige Kriterium erfüllen, wird als Pareto-kritische
Menge bezeichnet. Diese Arbeit enthält neue Resultate über Pareto-kritische Mengen
für glatte und nicht-glatte Mehrzieloptimierungsprobleme, sowohl was deren Berechnung
betrifft als auch deren Struktur. Im glatten Fall erfolgt die Berechnung über
ein Fortsetzungsverfahren, im nichtglatten Fall über ein Abstiegsverfahren. Anschließend
wird die Struktur des Randes der Pareto-kritischen Menge analysiert, welcher aus
Pareto-kritischen Mengen kleinerer Subprobleme besteht. Schlussendlich werden
inverse Probleme betrachtet, bei denen zu einer gegebenen Datenmenge ein Zielfunktionsvektor
gefunden werden soll, für den die Datenpunkte kritisch sind.
- lang: eng
text: Multiobjective optimization is concerned with the simultaneous optimization
of multiple scalar-valued functions. In this case, a point is optimal if there
is no other point that is at least as good in all objectives and better in at
least one objective. A necessary condition for optimality can be derived based
on first-order information of the objectives. The set of points that satisfy this
necessary condition is called the Pareto critical set. This thesis presents new
results about Pareto critical sets for smooth and nonsmooth multiobjective optimization
problems, both in terms of their efficient computation and structural properties.
In the smooth case they are computed via a continuation method and in the nonsmooth
case via a descent method. Afterwards, the structure of the boundary of the Pareto
critical set is analyzed, which consists of Pareto critical sets of smaller subproblems.
Finally, inverse problems are considered, where a data set is given and an objective
vector is sought for which the data points are critical.
author:
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
citation:
ama: Gebken B. Computation and Analysis of Pareto Critical Sets in Smooth and
Nonsmooth Multiobjective Optimization.; 2022. doi:10.17619/UNIPB/1-1327
apa: Gebken, B. (2022). Computation and analysis of Pareto critical sets in smooth
and nonsmooth multiobjective optimization. https://doi.org/10.17619/UNIPB/1-1327
bibtex: '@book{Gebken_2022, title={Computation and analysis of Pareto critical sets
in smooth and nonsmooth multiobjective optimization}, DOI={10.17619/UNIPB/1-1327},
author={Gebken, Bennet}, year={2022} }'
chicago: Gebken, Bennet. Computation and Analysis of Pareto Critical Sets in
Smooth and Nonsmooth Multiobjective Optimization, 2022. https://doi.org/10.17619/UNIPB/1-1327.
ieee: B. Gebken, Computation and analysis of Pareto critical sets in smooth and
nonsmooth multiobjective optimization. 2022.
mla: Gebken, Bennet. Computation and Analysis of Pareto Critical Sets in Smooth
and Nonsmooth Multiobjective Optimization. 2022, doi:10.17619/UNIPB/1-1327.
short: B. Gebken, Computation and Analysis of Pareto Critical Sets in Smooth and
Nonsmooth Multiobjective Optimization, 2022.
date_created: 2022-06-01T06:48:08Z
date_updated: 2022-06-01T07:13:09Z
department:
- _id: '101'
doi: 10.17619/UNIPB/1-1327
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://digital.ub.uni-paderborn.de/hs/download/pdf/6531779
oa: '1'
status: public
supervisor:
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
title: Computation and analysis of Pareto critical sets in smooth and nonsmooth multiobjective
optimization
type: dissertation
user_id: '32643'
year: '2022'
...
---
_id: '20731'
abstract:
- lang: eng
text: We present a novel algorithm that allows us to gain detailed insight into
the effects of sparsity in linear and nonlinear optimization, which is of great
importance in many scientific areas such as image and signal processing, medical
imaging, compressed sensing, and machine learning (e.g., for the training of neural
networks). Sparsity is an important feature to ensure robustness against noisy
data, but also to find models that are interpretable and easy to analyze due to
the small number of relevant terms. It is common practice to enforce sparsity
by adding the ℓ1-norm as a weighted penalty term. In order to gain a better understanding
and to allow for an informed model selection, we directly solve the corresponding
multiobjective optimization problem (MOP) that arises when we minimize the main
objective and the ℓ1-norm simultaneously. As this MOP is in general non-convex
for nonlinear objectives, the weighting method will fail to provide all optimal
compromises. To avoid this issue, we present a continuation method which is specifically
tailored to MOPs with two objective functions one of which is the ℓ1-norm. Our
method can be seen as a generalization of well-known homotopy methods for linear
regression problems to the nonlinear case. Several numerical examples - including
neural network training - demonstrate our theoretical findings and the additional
insight that can be gained by this multiobjective approach.
article_type: original
author:
- first_name: Katharina
full_name: Bieker, Katharina
id: '32829'
last_name: Bieker
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: Bieker K, Gebken B, Peitz S. On the Treatment of Optimization Problems with
L1 Penalty Terms via Multiobjective Continuation. IEEE Transactions on Pattern
Analysis and Machine Intelligence. 2022;44(11):7797-7808. doi:10.1109/TPAMI.2021.3114962
apa: 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
bibtex: '@article{Bieker_Gebken_Peitz_2022, title={On the Treatment of Optimization
Problems with L1 Penalty Terms via Multiobjective Continuation}, volume={44},
DOI={10.1109/TPAMI.2021.3114962},
number={11}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={IEEE}, author={Bieker, Katharina and Gebken, Bennet and Peitz, Sebastian},
year={2022}, pages={7797–7808} }'
chicago: 'Bieker, Katharina, Bennet Gebken, and Sebastian Peitz. “On the Treatment
of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation.”
IEEE Transactions on Pattern Analysis and Machine Intelligence 44, no.
11 (2022): 7797–7808. https://doi.org/10.1109/TPAMI.2021.3114962.'
ieee: 'K. Bieker, B. Gebken, and S. Peitz, “On the Treatment of Optimization Problems
with L1 Penalty Terms via Multiobjective Continuation,” IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 7797–7808,
2022, doi: 10.1109/TPAMI.2021.3114962.'
mla: Bieker, Katharina, et al. “On the Treatment of Optimization Problems with L1
Penalty Terms via Multiobjective Continuation.” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 44, no. 11, IEEE, 2022, pp. 7797–808,
doi:10.1109/TPAMI.2021.3114962.
short: K. Bieker, B. Gebken, S. Peitz, IEEE Transactions on Pattern Analysis and
Machine Intelligence 44 (2022) 7797–7808.
date_created: 2020-12-15T07:46:36Z
date_updated: 2022-10-21T12:27:16Z
ddc:
- '510'
department:
- _id: '101'
- _id: '530'
- _id: '655'
doi: 10.1109/TPAMI.2021.3114962
file:
- access_level: closed
content_type: application/pdf
creator: speitz
date_created: 2021-09-25T11:59:15Z
date_updated: 2021-09-25T11:59:15Z
file_id: '25040'
file_name: On_the_Treatment_of_Optimization_Problems_with_L1_Penalty_Terms_via_Multiobjective_Continuation.pdf
file_size: 7990831
relation: main_file
success: 1
file_date_updated: 2021-09-25T11:59:15Z
has_accepted_license: '1'
intvolume: ' 44'
issue: '11'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9547772
oa: '1'
page: 7797-7808
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: epub_ahead
publisher: IEEE
status: public
title: On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective
Continuation
type: journal_article
user_id: '47427'
volume: 44
year: '2022'
...
---
_id: '16867'
abstract:
- lang: eng
text: "In this article, we present an efficient descent method for locally Lipschitz\r\ncontinuous
multiobjective optimization problems (MOPs). The method is realized\r\nby combining
a theoretical result regarding the computation of descent\r\ndirections for nonsmooth
MOPs with a practical method to approximate the\r\nsubdifferentials of the objective
functions. We show convergence to points\r\nwhich satisfy a necessary condition
for Pareto optimality. Using a set of test\r\nproblems, we compare our method
to the multiobjective proximal bundle method by\r\nM\\\"akel\\\"a. The results
indicate that our method is competitive while being\r\neasier to implement. While
the number of objective function evaluations is\r\nlarger, the overall number
of subgradient evaluations is lower. Finally, we\r\nshow that our method can be
combined with a subdivision algorithm to compute\r\nentire Pareto sets of nonsmooth
MOPs."
author:
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
citation:
ama: Gebken B, Peitz S. An efficient descent method for locally Lipschitz multiobjective
optimization problems. Journal of Optimization Theory and Applications.
2021;188:696-723. doi:10.1007/s10957-020-01803-w
apa: 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
bibtex: '@article{Gebken_Peitz_2021, title={An efficient descent method for locally
Lipschitz multiobjective optimization problems}, volume={188}, DOI={10.1007/s10957-020-01803-w},
journal={Journal of Optimization Theory and Applications}, author={Gebken, Bennet
and Peitz, Sebastian}, year={2021}, pages={696–723} }'
chicago: 'Gebken, Bennet, and Sebastian Peitz. “An Efficient Descent Method for
Locally Lipschitz Multiobjective Optimization Problems.” Journal of Optimization
Theory and Applications 188 (2021): 696–723. https://doi.org/10.1007/s10957-020-01803-w.'
ieee: B. Gebken and S. Peitz, “An efficient descent method for locally Lipschitz
multiobjective optimization problems,” Journal of Optimization Theory and Applications,
vol. 188, pp. 696–723, 2021.
mla: Gebken, Bennet, and Sebastian Peitz. “An Efficient Descent Method for Locally
Lipschitz Multiobjective Optimization Problems.” Journal of Optimization Theory
and Applications, vol. 188, 2021, pp. 696–723, doi:10.1007/s10957-020-01803-w.
short: B. Gebken, S. Peitz, Journal of Optimization Theory and Applications 188
(2021) 696–723.
date_created: 2020-04-27T09:11:22Z
date_updated: 2022-01-06T06:52:57Z
department:
- _id: '101'
doi: 10.1007/s10957-020-01803-w
intvolume: ' 188'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/content/pdf/10.1007/s10957-020-01803-w.pdf
oa: '1'
page: 696-723
publication: Journal of Optimization Theory and Applications
publication_status: published
status: public
title: An efficient descent method for locally Lipschitz multiobjective optimization
problems
type: journal_article
user_id: '47427'
volume: 188
year: '2021'
...
---
_id: '16295'
abstract:
- lang: eng
text: It is a challenging task to identify the objectives on which a certain decision
was based, in particular if several, potentially conflicting criteria are equally
important and a continuous set of optimal compromise decisions exists. This task
can be understood as the inverse problem of multiobjective optimization, where
the goal is to find the objective function vector of a given Pareto set. To this
end, we present a method to construct the objective function vector of an unconstrained
multiobjective optimization problem (MOP) such that the Pareto critical set contains
a given set of data points with prescribed KKT multipliers. If such an MOP can
not be found, then the method instead produces an MOP whose Pareto critical set
is at least close to the data points. The key idea is to consider the objective
function vector in the multiobjective KKT conditions as variable and then search
for the objectives that minimize the Euclidean norm of the resulting system of
equations. By expressing the objectives in a finite-dimensional basis, we transform
this problem into a homogeneous, linear system of equations that can be solved
efficiently. Potential applications of this approach include the identification
of objectives (both from clean and noisy data) and the construction of surrogate
models for expensive MOPs.
author:
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: https://orcid.org/0000-0002-3389-793X
citation:
ama: 'Gebken B, Peitz S. Inverse multiobjective optimization: Inferring decision
criteria from data. Journal of Global Optimization. 2021;80:3-29. doi:10.1007/s10898-020-00983-z'
apa: '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'
bibtex: '@article{Gebken_Peitz_2021, title={Inverse multiobjective optimization:
Inferring decision criteria from data}, volume={80}, DOI={10.1007/s10898-020-00983-z},
journal={Journal of Global Optimization}, publisher={Springer}, author={Gebken,
Bennet and Peitz, Sebastian}, year={2021}, pages={3–29} }'
chicago: 'Gebken, Bennet, and Sebastian Peitz. “Inverse Multiobjective Optimization:
Inferring Decision Criteria from Data.” Journal of Global Optimization
80 (2021): 3–29. https://doi.org/10.1007/s10898-020-00983-z.'
ieee: 'B. Gebken and S. Peitz, “Inverse multiobjective optimization: Inferring decision
criteria from data,” Journal of Global Optimization, vol. 80, pp. 3–29,
2021.'
mla: 'Gebken, Bennet, and Sebastian Peitz. “Inverse Multiobjective Optimization:
Inferring Decision Criteria from Data.” Journal of Global Optimization,
vol. 80, Springer, 2021, pp. 3–29, doi:10.1007/s10898-020-00983-z.'
short: B. Gebken, S. Peitz, Journal of Global Optimization 80 (2021) 3–29.
date_created: 2020-03-13T12:45:05Z
date_updated: 2022-01-06T06:52:48Z
department:
- _id: '101'
doi: 10.1007/s10898-020-00983-z
intvolume: ' 80'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/content/pdf/10.1007/s10898-020-00983-z.pdf
oa: '1'
page: 3-29
publication: Journal of Global Optimization
publisher: Springer
status: public
title: 'Inverse multiobjective optimization: Inferring decision criteria from data'
type: journal_article
user_id: '47427'
volume: 80
year: '2021'
...
---
_id: '16712'
abstract:
- lang: eng
text: We investigate self-adjoint matrices A∈Rn,n with respect to their equivariance
properties. We show in particular that a matrix is self-adjoint if and only if
it is equivariant with respect to the action of a group Γ2(A)⊂O(n) which is isomorphic
to ⊗nk=1Z2. If the self-adjoint matrix possesses multiple eigenvalues – this may,
for instance, be induced by symmetry properties of an underlying dynamical system
– then A is even equivariant with respect to the action of a group Γ(A)≃∏ki=1O(mi)
where m1,…,mk are the multiplicities of the eigenvalues λ1,…,λk of A. We discuss
implications of this result for equivariant bifurcation problems, and we briefly
address further applications for the Procrustes problem, graph symmetries and
Taylor expansions.
author:
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Raphael
full_name: Gerlach, Raphael
id: '32655'
last_name: Gerlach
- first_name: Stefan
full_name: Klus, Stefan
last_name: Klus
citation:
ama: Dellnitz M, Gebken B, Gerlach R, Klus S. On the equivariance properties of
self-adjoint matrices. Dynamical Systems. 2020;35(2):197-215. doi:10.1080/14689367.2019.1661355
apa: 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
bibtex: '@article{Dellnitz_Gebken_Gerlach_Klus_2020, title={On the equivariance
properties of self-adjoint matrices}, volume={35}, DOI={10.1080/14689367.2019.1661355},
number={2}, journal={Dynamical Systems}, author={Dellnitz, Michael and Gebken,
Bennet and Gerlach, Raphael and Klus, Stefan}, year={2020}, pages={197–215} }'
chicago: 'Dellnitz, Michael, Bennet Gebken, Raphael Gerlach, and Stefan Klus. “On
the Equivariance Properties of Self-Adjoint Matrices.” Dynamical Systems
35, no. 2 (2020): 197–215. https://doi.org/10.1080/14689367.2019.1661355.'
ieee: 'M. Dellnitz, B. Gebken, R. Gerlach, and S. Klus, “On the equivariance properties
of self-adjoint matrices,” Dynamical Systems, vol. 35, no. 2, pp. 197–215,
2020, doi: 10.1080/14689367.2019.1661355.'
mla: Dellnitz, Michael, et al. “On the Equivariance Properties of Self-Adjoint Matrices.”
Dynamical Systems, vol. 35, no. 2, 2020, pp. 197–215, doi:10.1080/14689367.2019.1661355.
short: M. Dellnitz, B. Gebken, R. Gerlach, S. Klus, Dynamical Systems 35 (2020)
197–215.
date_created: 2020-04-16T14:07:25Z
date_updated: 2023-11-17T13:12:59Z
department:
- _id: '101'
doi: 10.1080/14689367.2019.1661355
intvolume: ' 35'
issue: '2'
language:
- iso: eng
main_file_link:
- url: https://doi.org/10.1080/14689367.2019.1661355
page: 197-215
publication: Dynamical Systems
publication_identifier:
issn:
- 1468-9367
- 1468-9375
publication_status: published
status: public
title: On the equivariance properties of self-adjoint matrices
type: journal_article
user_id: '32655'
volume: 35
year: '2020'
...
---
_id: '10595'
abstract:
- lang: eng
text: In this article we show that the boundary of the Pareto critical set of an
unconstrained multiobjective optimization problem (MOP) consists of Pareto critical
points of subproblems where only a subset of the set of objective functions is
taken into account. If the Pareto critical set is completely described by its
boundary (e.g., if we have more objective functions than dimensions in decision
space), then this can be used to efficiently solve the MOP by solving a number
of MOPs with fewer objective functions. If this is not the case, the results can
still give insight into the structure of the Pareto critical set.
article_type: original
author:
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: https://orcid.org/0000-0002-3389-793X
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
citation:
ama: Gebken B, Peitz S, Dellnitz M. On the hierarchical structure of Pareto critical
sets. Journal of Global Optimization. 2019;73(4):891-913. doi:10.1007/s10898-019-00737-6
apa: 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
bibtex: '@article{Gebken_Peitz_Dellnitz_2019, title={On the hierarchical structure
of Pareto critical sets}, volume={73}, DOI={10.1007/s10898-019-00737-6},
number={4}, journal={Journal of Global Optimization}, author={Gebken, Bennet and
Peitz, Sebastian and Dellnitz, Michael}, year={2019}, pages={891–913} }'
chicago: 'Gebken, Bennet, Sebastian Peitz, and Michael Dellnitz. “On the Hierarchical
Structure of Pareto Critical Sets.” Journal of Global Optimization 73,
no. 4 (2019): 891–913. https://doi.org/10.1007/s10898-019-00737-6.'
ieee: B. Gebken, S. Peitz, and M. Dellnitz, “On the hierarchical structure of Pareto
critical sets,” Journal of Global Optimization, vol. 73, no. 4, pp. 891–913,
2019.
mla: Gebken, Bennet, et al. “On the Hierarchical Structure of Pareto Critical Sets.”
Journal of Global Optimization, vol. 73, no. 4, 2019, pp. 891–913, doi:10.1007/s10898-019-00737-6.
short: B. Gebken, S. Peitz, M. Dellnitz, Journal of Global Optimization 73 (2019)
891–913.
date_created: 2019-07-10T08:13:31Z
date_updated: 2022-01-06T06:50:46Z
department:
- _id: '101'
doi: 10.1007/s10898-019-00737-6
intvolume: ' 73'
issue: '4'
language:
- iso: eng
page: 891-913
publication: Journal of Global Optimization
publication_identifier:
issn:
- 0925-5001
- 1573-2916
publication_status: published
status: public
title: On the hierarchical structure of Pareto critical sets
type: journal_article
user_id: '47427'
volume: 73
year: '2019'
...
---
_id: '8750'
abstract:
- lang: eng
text: In this article we propose a descent method for equality and inequality constrained
multiobjective optimization problems (MOPs) which generalizes the steepest descent
method for unconstrained MOPs by Fliege and Svaiter to constrained problems by
using two active set strategies. Under some regularity assumptions on the problem,
we show that accumulation points of our descent method satisfy a necessary condition
for local Pareto optimality. Finally, we show the typical behavior of our method
in a numerical example.
author:
- first_name: Bennet
full_name: Gebken, Bennet
id: '32643'
last_name: Gebken
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: https://orcid.org/0000-0002-3389-793X
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
citation:
ama: 'Gebken B, Peitz S, Dellnitz M. A Descent Method for Equality and Inequality
Constrained Multiobjective Optimization Problems. In: Numerical and Evolutionary
Optimization – NEO 2017. Cham; 2018. doi:10.1007/978-3-319-96104-0_2'
apa: 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
bibtex: '@inproceedings{Gebken_Peitz_Dellnitz_2018, place={Cham}, title={A Descent
Method for Equality and Inequality Constrained Multiobjective Optimization Problems},
DOI={10.1007/978-3-319-96104-0_2},
booktitle={Numerical and Evolutionary Optimization – NEO 2017}, author={Gebken,
Bennet and Peitz, Sebastian and Dellnitz, Michael}, year={2018} }'
chicago: Gebken, Bennet, Sebastian Peitz, and Michael Dellnitz. “A Descent Method
for Equality and Inequality Constrained Multiobjective Optimization Problems.”
In Numerical and Evolutionary Optimization – NEO 2017. Cham, 2018. https://doi.org/10.1007/978-3-319-96104-0_2.
ieee: B. Gebken, S. Peitz, and M. Dellnitz, “A Descent Method for Equality and Inequality
Constrained Multiobjective Optimization Problems,” in Numerical and Evolutionary
Optimization – NEO 2017, 2018.
mla: Gebken, Bennet, et al. “A Descent Method for Equality and Inequality Constrained
Multiobjective Optimization Problems.” Numerical and Evolutionary Optimization
– NEO 2017, 2018, doi:10.1007/978-3-319-96104-0_2.
short: 'B. Gebken, S. Peitz, M. Dellnitz, in: Numerical and Evolutionary Optimization
– NEO 2017, Cham, 2018.'
conference:
name: 'NEO 2017: Numerical and Evolutionary Optimization'
date_created: 2019-03-29T13:26:47Z
date_updated: 2022-01-06T07:04:00Z
department:
- _id: '101'
doi: 10.1007/978-3-319-96104-0_2
language:
- iso: eng
place: Cham
publication: Numerical and Evolutionary Optimization – NEO 2017
publication_identifier:
isbn:
- '9783319961033'
- '9783319961040'
issn:
- 1860-949X
- 1860-9503
publication_status: published
status: public
title: A Descent Method for Equality and Inequality Constrained Multiobjective Optimization
Problems
type: conference
user_id: '47427'
year: '2018'
...