4 Search Results for "Herold, Martin"

Document
DOI: 10.4230/LIPIcs.SoCG.2026.48
FPT Approximations for Capacitated Sum of Radii and Diameters

Authors: Arnold Filtser and Ameet Gadekar

Published in: LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026)

Abstract
The Capacitated Sum of Radii problem involves partitioning a set of points P, where each point p ∈ P has capacity U_p, into k clusters that minimize the sum of cluster radii, such that the number of points in the cluster centered at point p is at most U_p. We begin by showing that the problem is APX-hard, and that under gap-ETH there is no parameterized approximation scheme (FPT-AS). We then construct a ≈5.83-approximation algorithm in FPT time (improving a previous ≈7.61 approximation in FPT time). Our results also hold when the objective is a general monotone symmetric norm of radii. We also improve the approximation factors for the uniform capacity case, and for the closely related problem of Capacitated Sum of Diameters.
Cite as

Arnold Filtser and Ameet Gadekar. FPT Approximations for Capacitated Sum of Radii and Diameters. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 48:1-48:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{filtser_et_al:LIPIcs.SoCG.2026.48,
  author =	{Filtser, Arnold and Gadekar, Ameet},
  title =	{{FPT Approximations for Capacitated Sum of Radii and Diameters}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{48:1--48:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-418-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{367},
  editor =	{Ahn, Hee-Kap and Hoffmann, Michael and Nayyeri, Amir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.48},
  URN =		{urn:nbn:de:0030-drops-258545},
  doi =		{10.4230/LIPIcs.SoCG.2026.48},
  annote =	{Keywords: clustering, sum of radii, sum of diameter, capacitated clustering, fpt}
}
Document
DOI: 10.4230/LIPIcs.SoCG.2025.56
Sublinear Data Structures for Nearest Neighbor in Ultra High Dimensions

Authors: Martin G. Herold, Danupon Nanongkai, Joachim Spoerhase, Nithin Varma, and Zihang Wu

Published in: LIPIcs, Volume 332, 41st International Symposium on Computational Geometry (SoCG 2025)

Abstract
Geometric data structures have been extensively studied in the regime where the dimension is much smaller than the number of input points. But in many scenarios in Machine Learning, the dimension can be much higher than the number of points and can be so high that the data structure might be unable to read and store all coordinates of the input and query points. Inspired by these scenarios and related studies in feature selection and explainable clustering, we initiate the study of geometric data structures in this ultra-high dimensional regime. Our focus is the approximate nearest neighbor problem. In this problem, we are given a set of n points C ⊆ ℝ^d and have to produce a small data structure that can quickly answer the following query: given q ∈ ℝ^d, return a point c ∈ C that is approximately nearest to q, where the distance is under 𝓁₁, 𝓁₂, or other norms. Many groundbreaking (1+ε)-approximation algorithms have recently been discovered for 𝓁₁- and 𝓁₂-norm distances in the regime where d≪ n. The main question in this paper is: Is there a data structure with sublinear (o(nd)) space and sublinear (o(d)) query time when d≫ n? This question can be partially answered from the machine-learning literature: - For 𝓁₁-norm distances, an Õ(log(n))-approximation data structure with Õ(n log d) space and O(n) query time can be obtained from explainable clustering techniques [Dasgupta et al. ICML'20; Makarychev and Shan ICML'21; Esfandiari, Mirrokni, and Narayanan SODA'22; Gamlath et al. NeurIPS'21; Charikar and Hu SODA'22]. - For 𝓁₂-norm distances, a (√3+ε)-approximation data structure with Õ(n log(d)/poly(ε)) space and Õ(n/poly(ε)) query time can be obtained from feature selection techniques [Boutsidis, Drineas, and Mahoney NeurIPS'09; Boutsidis et al. IEEE Trans. Inf. Theory'15; Cohen et al. STOC'15]. - For 𝓁_p-norm distances, a O(n^{p-1}log²(n))-approximation data structure with O(nlog(n) + nlog(d)) space and O(n) query time can be obtained from the explainable clustering algorithms of [Gamlath et al. NeurIPS'21]. An important open problem is whether a (1+ε)-approximation data structure exists. This is not known for any norm, even with higher (e.g. poly(n)⋅ o(d)) space and query time. In this paper, we answer this question affirmatively. We present (1+ε)-approximation data structures with the following guarantees. - For 𝓁₁- and 𝓁₂-norm distances: Õ(n log(d)/poly(ε)) space and Õ(n/poly(ε)) query time. We show that these space and time bounds are tight up to poly (log n/ε) factors. - For 𝓁_p-norm distances: Õ(n² log(d) (log log(n)/ε)^p) space and Õ (n(log log(n)/ε)^p) query time. Via simple reductions, our data structures imply sublinear-in-d data structures for some other geometric problems; e.g. approximate orthogonal range search (in the style of [Arya and Mount SoCG'95]), furthest neighbor, and give rise to a sublinear O(1)-approximate representation of k-median and k-means clustering. We hope that this paper inspires future work on sublinear geometric data structures.
Cite as

Martin G. Herold, Danupon Nanongkai, Joachim Spoerhase, Nithin Varma, and Zihang Wu. Sublinear Data Structures for Nearest Neighbor in Ultra High Dimensions. In 41st International Symposium on Computational Geometry (SoCG 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 332, pp. 56:1-56:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{herold_et_al:LIPIcs.SoCG.2025.56,
  author =	{Herold, Martin G. and Nanongkai, Danupon and Spoerhase, Joachim and Varma, Nithin and Wu, Zihang},
  title =	{{Sublinear Data Structures for Nearest Neighbor in Ultra High Dimensions}},
  booktitle =	{41st International Symposium on Computational Geometry (SoCG 2025)},
  pages =	{56:1--56:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-370-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{332},
  editor =	{Aichholzer, Oswin and Wang, Haitao},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2025.56},
  URN =		{urn:nbn:de:0030-drops-232087},
  doi =		{10.4230/LIPIcs.SoCG.2025.56},
  annote =	{Keywords: sublinear data structure, approximate nearest neighbor}
}
Document
DOI: 10.4230/DagRep.13.7.96
Parameterized Approximation: Algorithms and Hardness (Dagstuhl Seminar 23291)

Authors: Karthik C. S., Parinya Chalermsook, Joachim Spoerhase, Meirav Zehavi, and Martin Herold

Published in: Dagstuhl Reports, Volume 13, Issue 7 (2024)

Abstract
Parameterization and approximation are two established approaches of coping with intractability in combinatorial optimization. In this Dagstuhl Seminar, we studied parameterized approximation as a relatively new algorithmic paradigm that combines these two popular research areas. In particular, we analyzed the solution quality (approximation ratio) as well as the running time of an algorithm in terms of a parameter that captures the "complexity" of a problem instance. While the field has grown and yielded some promising results, our understanding of the area is rather ad-hoc compared to our knowledge in approximation or parameterized algorithms alone. In this seminar, we brought together researchers from both communities in order to bridge this gap by accommodating the exchange and unification of scientific knowledge.
Cite as

Karthik C. S., Parinya Chalermsook, Joachim Spoerhase, Meirav Zehavi, and Martin Herold. Parameterized Approximation: Algorithms and Hardness (Dagstuhl Seminar 23291). In Dagstuhl Reports, Volume 13, Issue 7, pp. 96-107, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{c.s._et_al:DagRep.13.7.96,
  author =	{C. S., Karthik and Chalermsook, Parinya and Spoerhase, Joachim and Zehavi, Meirav and Herold, Martin},
  title =	{{Parameterized Approximation: Algorithms and Hardness (Dagstuhl Seminar 23291)}},
  pages =	{96--107},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{7},
  editor =	{C. S., Karthik and Chalermsook, Parinya and Spoerhase, Joachim and Zehavi, Meirav and Herold, Martin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.7.96},
  URN =		{urn:nbn:de:0030-drops-197764},
  doi =		{10.4230/DagRep.13.7.96},
  annote =	{Keywords: approximation algorithms, Hardness of approximation, Parameterized algorithms}
}
Document
DOI: 10.4230/LIPIcs.MFCS.2023.44
Upward Translation of Optimal and P-Optimal Proof Systems in the Boolean Hierarchy over NP

Authors: Fabian Egidy, Christian Glaßer, and Martin Herold

Published in: LIPIcs, Volume 272, 48th International Symposium on Mathematical Foundations of Computer Science (MFCS 2023)

Abstract
We study the existence of optimal and p-optimal proof systems for classes in the Boolean hierarchy over NP. Our main results concern DP, i.e., the second level of this hierarchy: - If all sets in DP have p-optimal proof systems, then all sets in coDP have p-optimal proof systems. - The analogous implication for optimal proof systems fails relative to an oracle. As a consequence, we clarify such implications for all classes 𝒞 and 𝒟 in the Boolean hierarchy over NP: either we can prove the implication or show that it fails relative to an oracle. Furthermore, we show that the sets SAT and TAUT have p-optimal proof systems, if and only if all sets in the Boolean hierarchy over NP have p-optimal proof systems which is a new characterization of a conjecture studied by Pudlák.
Cite as

Fabian Egidy, Christian Glaßer, and Martin Herold. Upward Translation of Optimal and P-Optimal Proof Systems in the Boolean Hierarchy over NP. In 48th International Symposium on Mathematical Foundations of Computer Science (MFCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 272, pp. 44:1-44:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{egidy_et_al:LIPIcs.MFCS.2023.44,
  author =	{Egidy, Fabian and Gla{\ss}er, Christian and Herold, Martin},
  title =	{{Upward Translation of Optimal and P-Optimal Proof Systems in the Boolean Hierarchy over NP}},
  booktitle =	{48th International Symposium on Mathematical Foundations of Computer Science (MFCS 2023)},
  pages =	{44:1--44:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-292-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{272},
  editor =	{Leroux, J\'{e}r\^{o}me and Lombardy, Sylvain and Peleg, David},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2023.44},
  URN =		{urn:nbn:de:0030-drops-185784},
  doi =		{10.4230/LIPIcs.MFCS.2023.44},
  annote =	{Keywords: Computational Complexity, Boolean Hierarchy, Proof Complexity, Proof Systems, Oracle Construction}
}
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