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Documents authored by Ohnesorge, Felix

Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2026.116
A Tight Double-Exponential Lower Bound for High-Multiplicity Bin Packing

Authors: Klaus Jansen, Felix Ohnesorge, and Lis Pirotton

Published in: LIPIcs, Volume 374, 53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026)

Abstract
Consider a high-multiplicity Bin Packing instance I with d distinct item types. In 2014, Goemans and Rothvoss gave an algorithm with runtime (|I|²)^O(d) for this problem [SODA'14], where |I| denotes the encoding length of the instance I. Although Jansen and Klein [SODA'17] later developed an algorithm that improves upon this runtime in a special case, it has remained a major open problem by Goemans and Rothvoss [J.ACM'20] whether the doubly exponential dependency on d is necessary. We solve this open problem by showing that unless the Exponential Time Hypothesis (ETH) fails, there is no algorithm solving the high-multiplicity Bin Packing problem in time (|I|²)^o(d). To prove this, we introduce a novel reduction from 3-SAT. The core of our construction is efficiently encoding all information from a 3-SAT instance with n variables into an ILP with O(log n) variables and constraints. This result confirms that the Goemans and Rothvoss algorithm is essentially best-possible for Bin Packing parameterized by the number d of item sizes in the context of XP time algorithms.
Cite as

Klaus Jansen, Felix Ohnesorge, and Lis Pirotton. A Tight Double-Exponential Lower Bound for High-Multiplicity Bin Packing. In 53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 374, pp. 116:1-116:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{jansen_et_al:LIPIcs.ICALP.2026.116,
  author =	{Jansen, Klaus and Ohnesorge, Felix and Pirotton, Lis},
  title =	{{A Tight Double-Exponential Lower Bound for High-Multiplicity Bin Packing}},
  booktitle =	{53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026)},
  pages =	{116:1--116:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-428-4},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{374},
  editor =	{Bhattacharya, Sayan and Nanongkai, Danupon and Benedikt, Michael and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2026.116},
  URN =		{urn:nbn:de:0030-drops-265051},
  doi =		{10.4230/LIPIcs.ICALP.2026.116},
  annote =	{Keywords: Bin Packing, Lower Bound, Computational Complexity, ETH}
}
Document
DOI: 10.4230/LIPIcs.STACS.2026.56
A Practical 73/50 Approximation for Contiguous Monotone Moldable Job Scheduling

Authors: Klaus Jansen and Felix Ohnesorge

Published in: LIPIcs, Volume 364, 43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026)

Abstract
In moldable job scheduling, we are provided m identical machines and n jobs that can be executed on a variable number of machines. The execution time of each job depends on the number of machines assigned to execute that job. For the specific problem of monotone moldable job scheduling, jobs are assumed to have a processing time that is non-increasing in the number of machines. The previous best-known algorithms are: (1) a Polynomial Time Approximation Scheme (PTAS) with time complexity Ω(n^{g(1/ε)}), where g(⋅) is a super-exponential function [Jansen and Thöle '08; Jansen and Land '18], (2) a Fully Polynomial Time Approximation Scheme (FPTAS) for the case of m ≥ 8n/(ε) [Jansen and Land '18], and (3) a 3/2 approximation with time complexity O(nmlog(mn)) [Wu, Zhang, and Chen '23]. We present a new practically efficient algorithm with an approximation ratio of ≈ (1.4593 + ε) and a time complexity of O(nm log 1/(ε)). Our result also applies to the contiguous variant of the problem. In addition to our theoretical results, we implement the presented algorithm and show that the practical performance is significantly better than the theoretical worst-case approximation ratio.
Cite as

Klaus Jansen and Felix Ohnesorge. A Practical 73/50 Approximation for Contiguous Monotone Moldable Job Scheduling. In 43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 364, pp. 56:1-56:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{jansen_et_al:LIPIcs.STACS.2026.56,
  author =	{Jansen, Klaus and Ohnesorge, Felix},
  title =	{{A Practical 73/50 Approximation for Contiguous Monotone Moldable Job Scheduling}},
  booktitle =	{43rd International Symposium on Theoretical Aspects of Computer Science (STACS 2026)},
  pages =	{56:1--56:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-412-3},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{364},
  editor =	{Mahajan, Meena and Manea, Florin and McIver, Annabelle and Thắng, Nguy\~{ê}n Kim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2026.56},
  URN =		{urn:nbn:de:0030-drops-255453},
  doi =		{10.4230/LIPIcs.STACS.2026.56},
  annote =	{Keywords: computing, machine scheduling, moldable, polynomial approximation}
}
Document
DOI: 10.4230/LIPIcs.SEA.2024.13
3/2-Dual Approximation for CPU/GPU Scheduling

Authors: Bernhard Sebastian Germann, Klaus Jansen, Felix Ohnesorge, and Malte Tutas

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)

Abstract
We present a fast and efficient 3/2 dual approximation algorithm for CPU/GPU scheduling under the objective of makespan minimization. In CPU/GPU scheduling tasks can be scheduled on two different architectures. When executed on the CPU, a task is moldable and can be assigned to multiple cores. The running time becomes a function in the assigned cores. On a GPU, the task is a classical job with a set processing time. Both settings have drawn recent independent scientific interest. For the moldable CPU scheduling, the current best known constant rate approximation is a 3/2 approximation algorithm [Wu et al. EJOR volume 306]. The best efficient algorithm for this setting is a 3/2+ε approximation [Mounie et al. SIAM '07] whereas GPU scheduling admits a 13/11 approximation [Coffman, Garey, Johnson SIAM'78]. We improve upon the current best known algorithms for CPU/GPU scheduling by Bleuse et al. by formulating a novel multidimensional multiple choice knapsack to allot tasks to either architecture and schedule them there with known algorithms. This yields an improved running time over the current state of the art. We complement our theoretical results with experimentation that shows a significant speedup by using practical optimizations and explore their efficacy.
Cite as

Bernhard Sebastian Germann, Klaus Jansen, Felix Ohnesorge, and Malte Tutas. 3/2-Dual Approximation for CPU/GPU Scheduling. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 13:1-13:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{germann_et_al:LIPIcs.SEA.2024.13,
  author =	{Germann, Bernhard Sebastian and Jansen, Klaus and Ohnesorge, Felix and Tutas, Malte},
  title =	{{3/2-Dual Approximation for CPU/GPU Scheduling}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{13:1--13:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.13},
  URN =		{urn:nbn:de:0030-drops-203782},
  doi =		{10.4230/LIPIcs.SEA.2024.13},
  annote =	{Keywords: computing, machine scheduling, moldable, CPU/GPU}
}
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