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Sebastian Peitz

Paderborn University

42 papers hereh-index 221.9k citations69 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author8
  • middle author20
  • last author13

Across the 42 of 42 papers where every author was matched, so the position is known.

fields
  • math.OC24
  • math.DS8
  • cs.LG6
  • math.NA1
  • math.PR1
  • quant-ph1
affiliations
  • Paderborn University
Homepage
same name
  • Sebastian Peitz — 9 papers, h 3
  • Sebastian Peitz — 7 papers, h 4
  • Sebastian Peitz — 4 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162026
most citedKoopman analysis of quantum systems

17 citations · 34 across the 20 of their papers we have counts for

collaborators
Showing 2021 · math.OCShow all

4 papers · 2 filters

math.OC2021

On the structure of regularization paths for piecewise differentiable regularization terms

Bennet Gebken, Katharina Bieker, Sebastian Peitz

Regularization is used in many different areas of optimization when solutions are sought which not only minimize a given function, but also possess a certain degree of regularity.…

math.OC2021

Finite-data error bounds for Koopman-based prediction and control

Feliks Nüske, Sebastian Peitz, Friedrich Philipp +2

The Koopman operator has become an essential tool for data-driven approximation of dynamical (control) systems, e.g., via extended dynamic mode decomposition. Despite its popularit…

math.OC2021

Derivative-Free Multiobjective Trust Region Descent Method Using Radial Basis Function Surrogate Models

Manuel Berkemeier, Sebastian Peitz

We present a flexible trust region descend algorithm for unconstrained and convexly constrained multiobjective optimization problems. It is targeted at heterogeneous and expensive…

math.OC2021

On the Universal Transformation of Data-Driven Models to Control Systems

Sebastian Peitz, Katharina Bieker

The advances in data science and machine learning have resulted in significant improvements regarding the modeling and simulation of nonlinear dynamical systems. It is nowadays pos…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.