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Documents authored by Lindermayr, Alexander

Document
DOI: 10.4230/LIPIcs.ESA.2026.128
Online Flow Time Minimization with Gradually Revealed Jobs

Authors: Alexander Lindermayr, Guido Schäfer, Jens Schlöter, and Leen Stougie

Published in: LIPIcs, Volume 388, 34th Annual European Symposium on Algorithms (ESA 2026)

Abstract
We consider the problem of online preemptive scheduling on a single machine to minimize the total flow time. In clairvoyant scheduling, where job processing times are revealed upon arrival, the Shortest Remaining Processing Time (SRPT) algorithm is optimal. In practice, however, exact processing times are often unknown. At the opposite extreme, non-clairvoyant scheduling, in which processing times are revealed only upon completion, suffers from strong lower bounds on the competitive ratio. This motivates the study of intermediate information models. We introduce a new model in which processing times are revealed gradually during execution. Each job consists of a sequence of operations, and the processing time of an operation becomes known only after the preceding one completes. This models many scheduling scenarios that arise in computing systems. Our main result is a deterministic O(m²)-competitive algorithm, where m is the maximum number of operations per job. More specifically, we prove a refined competitive ratio in O(m₁ ⋅ m₂), where m₁ and m₂ are instance-dependent parameters describing the operation size structure. Our algorithm and analysis build on recent advancements in robust flow time minimization (SODA '26), where jobs arrive with estimated sizes. However, in our setting we have no bounded estimate on a job’s processing time. Thus, we design a highly adaptive algorithm that gradually explores a job’s operations while working on them, and groups them into virtual chunks whose size can be well-estimated. This is a crucial ingredient of our result and requires a much more careful analysis compared to the robust setting. We also provide lower bounds showing that our bounds are essentially best possible. For the special case of scheduling with uniform obligatory tests, we show that SRPT at the operation level is 2-competitive, which is best possible.
Cite as

Alexander Lindermayr, Guido Schäfer, Jens Schlöter, and Leen Stougie. Online Flow Time Minimization with Gradually Revealed Jobs. In 34th Annual European Symposium on Algorithms (ESA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 388, pp. 128:1-128:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{lindermayr_et_al:LIPIcs.ESA.2026.128,
  author =	{Lindermayr, Alexander and Sch\"{a}fer, Guido and Schl\"{o}ter, Jens and Stougie, Leen},
  title =	{{Online Flow Time Minimization with Gradually Revealed Jobs}},
  booktitle =	{34th Annual European Symposium on Algorithms (ESA 2026)},
  pages =	{128:1--128:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-445-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{388},
  editor =	{Bille, Philip and Pettie, Seth and Storandt, Sabine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2026.128},
  URN =		{urn:nbn:de:0030-drops-272649},
  doi =		{10.4230/LIPIcs.ESA.2026.128},
  annote =	{Keywords: optimization, scheduling, online algorithms, competitive analysis, flow time, non-clairvoyance}
}
Document
DOI: 10.4230/DagRep.15.3.94
Scheduling (Dagstuhl Seminar 25121)

Authors: Claire Mathieu, Nicole Megow, Benjamin J. Moseley, Frits C. R. Spieksma, and Alexander Lindermayr

Published in: Dagstuhl Reports, Volume 15, Issue 3 (2025)

Abstract
This report documents the program and outcomes of Dagstuhl Seminar 25121, "Scheduling". The seminar focused on bridging traditional algorithmic scheduling with the emerging field of fairness in resource allocation. Scheduling is a longstanding research area that has been studied from both practical and theoretical perspectives in computer science, mathematical optimization, and operations research for over 70 years. Fairness has become a key concern in recent years, particularly in the context of resource allocation and scheduling, where it naturally arises in applications such as kidney exchange, school choice, and political districting. The seminar centered on three main themes: (1) fair allocation, (2) fairness versus quality of service, and (3) modeling aspects of fairness in scheduling.
Cite as

Claire Mathieu, Nicole Megow, Benjamin J. Moseley, Frits C. R. Spieksma, and Alexander Lindermayr. Scheduling (Dagstuhl Seminar 25121). In Dagstuhl Reports, Volume 15, Issue 3, pp. 94-112, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{mathieu_et_al:DagRep.15.3.94,
  author =	{Mathieu, Claire and Megow, Nicole and Moseley, Benjamin J. and Spieksma, Frits C. R. and Lindermayr, Alexander},
  title =	{{Scheduling (Dagstuhl Seminar 25121)}},
  pages =	{94--112},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2025},
  volume =	{15},
  number =	{3},
  editor =	{Mathieu, Claire and Megow, Nicole and Moseley, Benjamin J. and Spieksma, Frits C. R. and Lindermayr, Alexander},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.3.94},
  URN =		{urn:nbn:de:0030-drops-248981},
  doi =		{10.4230/DagRep.15.3.94},
  annote =	{Keywords: scheduling, fairness, mathematical optimization, algorithms and complexity, uncertainty}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2022.99
Double Coverage with Machine-Learned Advice

Authors: Alexander Lindermayr, Nicole Megow, and Bertrand Simon

Published in: LIPIcs, Volume 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022)

Abstract
We study the fundamental online k-server problem in a learning-augmented setting. While in the traditional online model, an algorithm has no information about the request sequence, we assume that there is given some advice (e.g. machine-learned predictions) on an algorithm’s decision. There is, however, no guarantee on the quality of the prediction and it might be far from being correct. Our main result is a learning-augmented variation of the well-known Double Coverage algorithm for k-server on the line (Chrobak et al., SIDMA 1991) in which we integrate predictions as well as our trust into their quality. We give an error-dependent competitive ratio, which is a function of a user-defined confidence parameter, and which interpolates smoothly between an optimal consistency, the performance in case that all predictions are correct, and the best-possible robustness regardless of the prediction quality. When given good predictions, we improve upon known lower bounds for online algorithms without advice. We further show that our algorithm achieves for any k an almost optimal consistency-robustness tradeoff, within a class of deterministic algorithms respecting local and memoryless properties. Our algorithm outperforms a previously proposed (more general) learning-augmented algorithm. It is remarkable that the previous algorithm crucially exploits memory, whereas our algorithm is memoryless. Finally, we demonstrate in experiments the practicability and the superior performance of our algorithm on real-world data.
Cite as

Alexander Lindermayr, Nicole Megow, and Bertrand Simon. Double Coverage with Machine-Learned Advice. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 99:1-99:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{lindermayr_et_al:LIPIcs.ITCS.2022.99,
  author =	{Lindermayr, Alexander and Megow, Nicole and Simon, Bertrand},
  title =	{{Double Coverage with Machine-Learned Advice}},
  booktitle =	{13th Innovations in Theoretical Computer Science Conference (ITCS 2022)},
  pages =	{99:1--99:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-217-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{215},
  editor =	{Braverman, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2022.99},
  URN =		{urn:nbn:de:0030-drops-156954},
  doi =		{10.4230/LIPIcs.ITCS.2022.99},
  annote =	{Keywords: online k-server problem, competitive analysis, learning-augmented algorithms, untrusted predictions, consistency, robustness}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2020.65
Elimination Distance to Bounded Degree on Planar Graphs

Authors: Alexander Lindermayr, Sebastian Siebertz, and Alexandre Vigny

Published in: LIPIcs, Volume 170, 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020)

Abstract
We study the graph parameter elimination distance to bounded degree, which was introduced by Bulian and Dawar in their study of the parameterized complexity of the graph isomorphism problem. We prove that the problem is fixed-parameter tractable on planar graphs, that is, there exists an algorithm that given a planar graph G and integers d and k decides in time f(k,d)⋅ n^c for a computable function f and constant c whether the elimination distance of G to the class of degree d graphs is at most k.
Cite as

Alexander Lindermayr, Sebastian Siebertz, and Alexandre Vigny. Elimination Distance to Bounded Degree on Planar Graphs. In 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 170, pp. 65:1-65:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{lindermayr_et_al:LIPIcs.MFCS.2020.65,
  author =	{Lindermayr, Alexander and Siebertz, Sebastian and Vigny, Alexandre},
  title =	{{Elimination Distance to Bounded Degree on Planar Graphs}},
  booktitle =	{45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020)},
  pages =	{65:1--65:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-159-7},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{170},
  editor =	{Esparza, Javier and Kr\'{a}l', Daniel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2020.65},
  URN =		{urn:nbn:de:0030-drops-127557},
  doi =		{10.4230/LIPIcs.MFCS.2020.65},
  annote =	{Keywords: Elimination distance, parameterized complexity, structural graph theory}
}

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