2 Search Results for "Rechner, Steffen"

Document
DOI: 10.4230/LIPIcs.SEA.2026.2
Different Scales of Randomness: Empirical Mixing Times of the Edge Switching and Curveball MCMC

Authors: Deepak Ajwani, Melvin Kallmayer, Alexander Leonhardt, Ulrich Meyer, Ryan O'Connor, and Manuel Penschuck

Published in: LIPIcs, Volume 371, 24th International Symposium on Experimental Algorithms (SEA 2026)

Abstract
The Fixed Degree Sequence Model (FDSM) asks for a uniform sample from the set of all simple graphs that match a prescribed degree sequence. It is typically implemented using Markov-Chain Monte-Carlo (MCMC) processes, such as Edge Switching or Curveball (and their variants). Yet despite decades of research, rigorous bounds on the mixing times of such processes remain impractical. Consequently, several experimental techniques have been used to derive "empirical lower bounds" on the mixing time. We address the following research questions: (1) Which commonly studied graph-theoretic properties serve as reliable empirical predictors for mixing of FDSM MCMC processes? (2) At what structural scales do these properties operate primarily (i. e., are they predominantly local or global in nature)? (3) How can these properties be characterised and quantified most effectively? To this end, we propose Claim, a novel systematic method to establish empirical lower bounds using learnt classifiers, and compare it to existing methods. Apart from interesting insights into the usage of machine learning for this problem, we also derive robust graph properties with respect to different randomisation algorithms. Although experimental in nature, these results may influence both theorist’s and algorithm engineer’s work on improved bounds and better algorithm respectively.
Cite as

Deepak Ajwani, Melvin Kallmayer, Alexander Leonhardt, Ulrich Meyer, Ryan O'Connor, and Manuel Penschuck. Different Scales of Randomness: Empirical Mixing Times of the Edge Switching and Curveball MCMC. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 2:1-2:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{ajwani_et_al:LIPIcs.SEA.2026.2,
  author =	{Ajwani, Deepak and Kallmayer, Melvin and Leonhardt, Alexander and Meyer, Ulrich and O'Connor, Ryan and Penschuck, Manuel},
  title =	{{Different Scales of Randomness: Empirical Mixing Times of the Edge Switching and Curveball MCMC}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{2:1--2:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-422-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{371},
  editor =	{Aum\"{u}ller, Martin and Finocchi, Irene},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.2},
  URN =		{urn:nbn:de:0030-drops-260062},
  doi =		{10.4230/LIPIcs.SEA.2026.2},
  annote =	{Keywords: Mixing Time, Graph Randomization, Machine Learning, Edge Switching}
}
Document
DOI: 10.4230/OASIcs.ATMOS.2014.122
Timing of Train Disposition: Towards Early Passenger Rerouting in Case of Delays

Authors: Martin Lemnian, Ralf Rückert, Steffen Rechner, Christoph Blendinger, and Matthias Müller-Hannemann

Published in: OASIcs, Volume 42, 14th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (2014)

Abstract
Passenger-friendly train disposition is a challenging, highly complex online optimization problem with uncertain and incomplete information about future delays. In this paper we focus on the timing within the disposition process. We introduce three different classification schemes to predict as early as possible the status of a transfer: whether it will almost surely break, is so critically delayed that it requires manual disposition, or can be regarded as only slightly uncertain or as being safe. The three approaches use lower bounds on travel times, historical distributions of delay data, and fuzzy logic, respectively. In experiments with real delay data we achieve an excellent classification rate. Furthermore, using realistic passenger flows we observe that there is a significant potential to reduce the passenger delay if an early rerouting strategy is applied.
Cite as

Martin Lemnian, Ralf Rückert, Steffen Rechner, Christoph Blendinger, and Matthias Müller-Hannemann. Timing of Train Disposition: Towards Early Passenger Rerouting in Case of Delays. In 14th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems. Open Access Series in Informatics (OASIcs), Volume 42, pp. 122-137, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{lemnian_et_al:OASIcs.ATMOS.2014.122,
  author =	{Lemnian, Martin and R\"{u}ckert, Ralf and Rechner, Steffen and Blendinger, Christoph and M\"{u}ller-Hannemann, Matthias},
  title =	{{Timing of Train Disposition: Towards Early Passenger Rerouting in Case of Delays}},
  booktitle =	{14th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems},
  pages =	{122--137},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-75-0},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{42},
  editor =	{Funke, Stefan and Mihal\'{a}k, Mat\'{u}s},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2014.122},
  URN =		{urn:nbn:de:0030-drops-47576},
  doi =		{10.4230/OASIcs.ATMOS.2014.122},
  annote =	{Keywords: train delays, event-activity model, timing of decisions, passenger flows, passenger rerouting}
}
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