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Volume

LIPIcs, Volume 80

44th International Colloquium on Automata, Languages, and Programming (ICALP 2017)

ICALP 2017, July 10-14, 2017, Warsaw, Poland

Editors: Ioannis Chatzigiannakis, Piotr Indyk, Fabian Kuhn, and Anca Muscholl

Document

DOI: 10.4230/LIPIcs.SEA.2026.4

Integer Programming Models for the Median of a 0-1 String Set Under Levenshtein Distance

Authors: Claudio Arbib, Andrea D'Ascenzo, Oya E. Karaşan, and Andrea Pizzuti

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

Abstract

The Median String Problem calls for finding a string that minimizes the average distance from a given set of strings. Under the Levenshtein (or edit) metric, the problem is NP-hard even for binary strings. We devised two novel integer linear programming models for this case and tested them against the only formulation we are aware of in the literature. Our numerical experiments attest to the efficacy of the proposed approach.

Cite as

Claudio Arbib, Andrea D'Ascenzo, Oya E. Karaşan, and Andrea Pizzuti. Integer Programming Models for the Median of a 0-1 String Set Under Levenshtein Distance. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{arbib_et_al:LIPIcs.SEA.2026.4,
  author =	{Arbib, Claudio and D'Ascenzo, Andrea and Kara\c{s}an, Oya E. and Pizzuti, Andrea},
  title =	{{Integer Programming Models for the Median of a 0-1 String Set Under Levenshtein Distance}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{4:1--4:15},
  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.2026.4},
  URN =		{urn:nbn:de:0030-drops-260081},
  doi =		{10.4230/LIPIcs.SEA.2026.4},
  annote =	{Keywords: Levenshtein Distance, Median String Problem, Integer Programming}
}

Document

DOI: 10.4230/LIPIcs.SEA.2026.11

Wavelet Forests Revisited

Authors: Eric Chiu and Dominik Kempa

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

Abstract

Rank and select queries are basic operations on sequences, with applications in compressed text indexes and other space-efficient data structures. One of the standard data structures supporting these queries is the wavelet tree. In this paper, we study wavelet forests, that is, wavelet-tree structures based on the fixed-block compression boosting technique. Such structures partition the input sequence into fixed-size blocks and build a separate wavelet tree for each block. Previous work showed that this approach yields strong practical performance for rank queries. We extend wavelet forests to support select queries. We show that select support can be added with little additional space overhead and that the resulting structures remain practically efficient. In experiments on a range of non-repetitive and repetitive inputs, wavelet forests are competitive with, and in most cases outperform, standalone wavelet-tree implementations. We also study the effect of internal parameters, including superblock size and navigational data, on select-query performance.

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Eric Chiu and Dominik Kempa. Wavelet Forests Revisited. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 11:1-11:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chiu_et_al:LIPIcs.SEA.2026.11,
  author =	{Chiu, Eric and Kempa, Dominik},
  title =	{{Wavelet Forests Revisited}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{11:1--11:11},
  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.2026.11},
  URN =		{urn:nbn:de:0030-drops-260152},
  doi =		{10.4230/LIPIcs.SEA.2026.11},
  annote =	{Keywords: wavelet tree, wavelet forest, select queries}
}

Document

DOI: 10.4230/LIPIcs.CPM.2026.12

Maximizing Diversity in (Near-)Median String Selection

Authors: Diptarka Chakraborty, Rudrayan Kundu, Nidhi Purohit, and Aravinda Kanchana Ruwanpathirana

Published in: LIPIcs, Volume 369, 37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026)

Abstract

Given a set of strings over a specified alphabet, identifying a median or consensus string that minimizes the total distance to all input strings is a fundamental data aggregation problem. When the Hamming distance is considered as the underlying metric, this problem has extensive applications, ranging from bioinformatics to pattern recognition. However, modern applications often require the generation of multiple (near-)optimal yet diverse median strings to enhance flexibility and robustness in decision-making. In this study, we address this need by focusing on two prominent diversity measures: sum dispersion and min dispersion. We first introduce an exact algorithm for the diameter variant of the problem, which identifies pairs of near-optimal medians that are maximally diverse. Subsequently, we propose a (1-ε)-approximation algorithm (for any ε > 0) for sum dispersion, as well as a bi-criteria approximation algorithm for the more challenging min dispersion case, allowing the generation of multiple (more than two) diverse near-optimal Hamming medians. Our approach primarily leverages structural insights into the Hamming median space and also draws on techniques from error-correcting code construction to establish these results.

Cite as

Diptarka Chakraborty, Rudrayan Kundu, Nidhi Purohit, and Aravinda Kanchana Ruwanpathirana. Maximizing Diversity in (Near-)Median String Selection. In 37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 369, pp. 12:1-12:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chakraborty_et_al:LIPIcs.CPM.2026.12,
  author =	{Chakraborty, Diptarka and Kundu, Rudrayan and Purohit, Nidhi and Ruwanpathirana, Aravinda Kanchana},
  title =	{{Maximizing Diversity in (Near-)Median String Selection}},
  booktitle =	{37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026)},
  pages =	{12:1--12:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-420-8},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{369},
  editor =	{Bille, Philip and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2026.12},
  URN =		{urn:nbn:de:0030-drops-259382},
  doi =		{10.4230/LIPIcs.CPM.2026.12},
  annote =	{Keywords: Diversity maximization, Hamming median, diameter, dispersion, approximation algorithms}
}

Document

DOI: 10.4230/LIPIcs.SWAT.2026.38

New Algorithms for Girth and Cycle Detection

Authors: Liam Roditty and Plia Trabelsi

Published in: LIPIcs, Volume 370, 20th Scandinavian Symposium on Algorithm Theory (SWAT 2026)

Abstract

Let G = (V,E) be an unweighted undirected graph with n vertices and m edges. Let g be the girth of G, that is, the length of a shortest cycle in G. We present a randomized algorithm with a running time of Õ(𝓁 ⋅ n^{1 + 1/(𝓁-ε)}) that returns a cycle of length at most 2𝓁 ⌈g/2⌉ - 2 ⌊ε⌈g/2⌉⌋, where 𝓁 ≥ 2 is an integer and ε ∈ [0,1], for every graph with g = polylog(n). Our algorithm generalizes an algorithm of Kadria et al. [SODA'22] that computes a cycle of length at most 4 ⌈g/2⌉ - 2 ⌊ε⌈g/2⌉⌋ in Õ(n^{1 + 1/(2 - ε)}) time. Kadria et al. presented also an algorithm that finds a cycle of length at most 2𝓁 ⌈g/2⌉ in Õ(n^{1 + 1/(𝓁)}) time, where 𝓁 must be an integer. Our algorithm generalizes this algorithm, as well, by replacing the integer parameter 𝓁 in the running time exponent with a real-valued parameter 𝓁 - ε, thereby offering greater flexibility in parameter selection and enabling a broader spectrum of combinations between running times and cycle lengths. We also show that for sparse graphs a better tradeoff is possible, by presenting an Õ(𝓁⋅ m^{1+ 1/(𝓁-ε)}) time randomized algorithm that returns a cycle of length at most 2𝓁(⌊(g-1)/2⌋) - 2(⌊ε⌊(g-1)/2⌋⌋+1), where 𝓁 ≥ 3 is an integer and ε ∈ [0,1), for every graph with g = polylog(n). To obtain our algorithms we develop several techniques and introduce a formal definition of hybrid cycle detection algorithms. Both may prove useful in broader contexts, including other cycle detection and approximation problems. Among our techniques is a new cycle searching technique, in which we search for a cycle from a given vertex and possibly all its neighbors in linear time. Using this technique together with more ideas we develop two hybrid algorithms. The first allows us to obtain an Õ(m^{2-2/(⌈g/2⌉+1))-time, (+1)-approximation of g. The second is used to obtain our Õ(𝓁⋅ n^{1+ 1/(𝓁-ε)})-time and Õ(𝓁⋅ m^{1+ 1/(𝓁-ε)})-time approximation algorithms.

Cite as

Liam Roditty and Plia Trabelsi. New Algorithms for Girth and Cycle Detection. In 20th Scandinavian Symposium on Algorithm Theory (SWAT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 370, pp. 38:1-38:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{roditty_et_al:LIPIcs.SWAT.2026.38,
  author =	{Roditty, Liam and Trabelsi, Plia},
  title =	{{New Algorithms for Girth and Cycle Detection}},
  booktitle =	{20th Scandinavian Symposium on Algorithm Theory (SWAT 2026)},
  pages =	{38:1--38:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-421-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{370},
  editor =	{Fraigniaud, Pierre},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SWAT.2026.38},
  URN =		{urn:nbn:de:0030-drops-260742},
  doi =		{10.4230/LIPIcs.SWAT.2026.38},
  annote =	{Keywords: Graph algorithms, All pairs shortest path, Girth, Cycle approximation}
}

Document

DOI: 10.4230/LIPIcs.CPM.2026.19

Asymmetric Streaming Approximate Pattern Matching

Authors: Wojciech Janczewski and Tatiana Starikovskaya

Published in: LIPIcs, Volume 369, 37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026)

Abstract

We study the space complexity of pattern matching in the asymmetric streaming model, focusing on approximate pattern matching under the Hamming and edit distances. In this problem, we are given an m-length pattern and an n-length text and must compute, for every position of the text, the smallest distance between the pattern and a substring of the text which ends at this position. In the asymmetric streaming model, we assume to have constant-time random access to the pattern, while the text arrives as a stream, one letter at a time. It is known that computing all distances exactly in the asymmetric streaming model requires Ω(m) space (for the edit distance see Li and Zheng [FSTTCS 2021]). Hence, to achieve sublinear space, a relaxation of the problem is necessary. One possible variant is to consider the small distance regime, where the algorithm must compute only those distances that are bounded by a small integer parameter k. In this case, existing algorithms in a more restrictive fully streaming model (Kociumaka, Clifford, Porat [SODA'19], Bhattacharya, Koucký [ICALP'23]) straightforwardly imply the existence of poly(k, log n)-space asymmetric streaming algorithms. Another possible relaxation is computing all distances approximately. For this variant, we don't have small-space algorithms in the fully streaming model: the best known algorithm solves pattern matching under the Hamming distance (1+ε)-approximately using 𝒪̃(ε^{-2}√m) space (Starikovskaya, Svagerka, Uznański [APPROX'20]). For the edit distance, no efficient approximation algorithms are known. In this work, we show approximation algorithms for pattern matching under the Hamming and edit distances in the asymmetric streaming model for any constant ε > 0: 1) We show that there is a simple randomised asymmetric streaming algorithm that solves approximate pattern matching under the Hamming distance (1+ε)-approximately using 𝒪(ε^{-3}log³n) bits. 2) As our second and main contribution, we extend the result of Cheng et al. [ICALP 2021] and show that for any integer k there is a deterministic asymmetric streaming algorithm that solves pattern matching under the edit distance (2^k-1+ε)-approximately using 𝒪̃(m^{1/k}) space.

Cite as

Wojciech Janczewski and Tatiana Starikovskaya. Asymmetric Streaming Approximate Pattern Matching. In 37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 369, pp. 19:1-19:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{janczewski_et_al:LIPIcs.CPM.2026.19,
  author =	{Janczewski, Wojciech and Starikovskaya, Tatiana},
  title =	{{Asymmetric Streaming Approximate Pattern Matching}},
  booktitle =	{37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026)},
  pages =	{19:1--19:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-420-8},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{369},
  editor =	{Bille, Philip and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2026.19},
  URN =		{urn:nbn:de:0030-drops-259458},
  doi =		{10.4230/LIPIcs.CPM.2026.19},
  annote =	{Keywords: Asymmetric streaming, Pattern matching, Approximation, Edit distance, Hamming distance}
}

Document

DOI: 10.4230/LIPIcs.CPM.2026.26

The Communication Complexity of Pattern Matching with Edits Revisited

Authors: Tomasz Kociumaka, Jakob Nogler, and Philip Wellnitz

Published in: LIPIcs, Volume 369, 37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026)

Abstract

The decades-old Pattern Matching with Edits problem, given a length-n string T (the text), a length-m string P (the pattern), and a positive integer k (the threshold), asks to list the k-error occurrences of P in T, that is, all fragments of T whose edit distance to P is at most k. The one-way communication complexity of this problem is the minimum number of bits that Alice, given an instance (P,T,k) of the problem, must send to Bob so that Bob can reconstruct the answer solely from that message. In recent work [STOC'24], we showed that, in the natural parameter regime 0 < k < m < n/2, Ω(n/m ⋅ k log(m/k)) bits are necessary and 𝒪(n/m ⋅ k log² m) bits are sufficient for this problem. More generally, for strings over an alphabet Σ, we gave an 𝒪(n/m ⋅ k log m log(m|Σ|))-bit encoding that allows one to recover a shortest sequence of edits for every k-error occurrence of P in T. In this paper, we revisit the original proof and improve the encoding size to 𝒪(n/m ⋅ k log (m|Σ|/k)), which matches the lower bound for constant-sized alphabets. We further establish a new tight lower bound of Ω(n/m ⋅ k log(m|Σ|/k)) for the edit sequence reporting variant we solve. Our encoding size also matches the communication complexity established for the simpler Pattern Matching with Mismatches problem in the context of streaming algorithms [Clifford, Kociumaka, Porat; SODA'19].

Cite as

Tomasz Kociumaka, Jakob Nogler, and Philip Wellnitz. The Communication Complexity of Pattern Matching with Edits Revisited. In 37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 369, pp. 26:1-26:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{kociumaka_et_al:LIPIcs.CPM.2026.26,
  author =	{Kociumaka, Tomasz and Nogler, Jakob and Wellnitz, Philip},
  title =	{{The Communication Complexity of Pattern Matching with Edits Revisited}},
  booktitle =	{37th Annual Symposium on Combinatorial Pattern Matching (CPM 2026)},
  pages =	{26:1--26:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-420-8},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{369},
  editor =	{Bille, Philip and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CPM.2026.26},
  URN =		{urn:nbn:de:0030-drops-259525},
  doi =		{10.4230/LIPIcs.CPM.2026.26},
  annote =	{Keywords: Edit distance, Pattern matching, Communication complexity}
}

Document

DOI: 10.4230/LIPIcs.SoCG.2026.31

Linear-Time (1+ε)-Approximation Algorithms for Two-Line-Center Problems

Authors: Chaeyoon Chung, Anil Maheshwari, and Michiel Smid

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

Abstract

Given a set S of n points in the plane, we study the two-line-center problem: finding two lines that minimize the maximum distance from each point in S to its closest line. We present a (1+ε)-approximation algorithm for the two-line-center problem that runs in O((n/ε) log (1/ε)) time, which improves the previously best O(nlog n + (n/ε²) log (1/ε) + (1/ε³)log (1/ε))-time algorithm. We also consider three variants of this problem, in which the orientations of the two lines are restricted: (1) the orientation of one of the two lines is fixed, (2) the orientations of both lines are fixed, and (3) the two lines are required to be parallel. For each of these three variants, we give the first (1+ε)-approximation algorithm that runs in linear time. In particular, for the variant where the orientation of one of the two lines is fixed, we also give an improved exact algorithm that runs in O(n log n) time and show that it is optimal.

Cite as

Chaeyoon Chung, Anil Maheshwari, and Michiel Smid. Linear-Time (1+ε)-Approximation Algorithms for Two-Line-Center Problems. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 31:1-31:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{chung_et_al:LIPIcs.SoCG.2026.31,
  author =	{Chung, Chaeyoon and Maheshwari, Anil and Smid, Michiel},
  title =	{{Linear-Time (1+\epsilon)-Approximation Algorithms for Two-Line-Center Problems}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{31:1--31:17},
  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.2026.31},
  URN =		{urn:nbn:de:0030-drops-258374},
  doi =		{10.4230/LIPIcs.SoCG.2026.31},
  annote =	{Keywords: Approximation algorithm, two-line-center problem, k-line-center problem, projective clustering, \epsilon-certificate, \epsilon-coreset, width of a point set}
}

Document

DOI: 10.4230/LIPIcs.SoCG.2026.33

Near-Optimal Bounds for Parameterized Euclidean k-Means

Authors: Vincent Cohen-Addad, Karthik C. S., David Saulpic, and Chris Schwiegelshohn

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

Abstract

The k-means problem is a classic objective for modeling clustering in a metric space. Given a set of points in a metric space, the goal is to find k representative points so as to minimize the sum of the squared distances from each point to its closest representative. In this work, we study the approximability of k-means in Euclidean spaces parameterized by the number of clusters, k. In seminal works, de la Vega, Karpinski, Kenyon, and Rabani [STOC'03] and Kumar, Sabharwal, and Sen [JACM'10] showed how to obtain a (1+ε)-approximation for high-dimensional Euclidean k-means in time 2^{(k/ε)^O(1)} ⋅ dn^O(1). In this work, we introduce a new fine-grained hypothesis called Exponential Time for Expanders Hypothesis (XXH) which roughly asserts that there are no non-trivial exponential time approximation algorithms for the vertex cover problem on near perfect vertex expanders. Assuming XXH, we close the above long line of work on approximating Euclidean k-means by showing that there is no 2^{(k/ε)^{1-o(1)}} ⋅ n^O(1) time algorithm achieving a (1+ε)-approximation for k-means in Euclidean space. This lower bound is tight as it matches the algorithm given by Feldman, Monemizadeh, and Sohler [SoCG'07] whose runtime is 2^O(k/ε) + O(ndk). Furthermore, assuming XXH, we show that the seminal O(n^{kd+1}) runtime exact algorithm of Inaba, Katoh, and Imai [SoCG'94] for k-means is optimal for small values of k.

Cite as

Vincent Cohen-Addad, Karthik C. S., David Saulpic, and Chris Schwiegelshohn. Near-Optimal Bounds for Parameterized Euclidean k-Means. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 33:1-33:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{cohenaddad_et_al:LIPIcs.SoCG.2026.33,
  author =	{Cohen-Addad, Vincent and C. S., Karthik and Saulpic, David and Schwiegelshohn, Chris},
  title =	{{Near-Optimal Bounds for Parameterized Euclidean k-Means}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{33:1--33:17},
  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.2026.33},
  URN =		{urn:nbn:de:0030-drops-258391},
  doi =		{10.4230/LIPIcs.SoCG.2026.33},
  annote =	{Keywords: k-means clustering, Euclidean space, Fine-Grained Complexity}
}

Document

DOI: 10.4230/LIPIcs.SoCG.2026.34

Almost-Optimal Upper and Lower Bounds for Clustering in Low Dimensional Euclidean Spaces

Authors: Vincent Cohen-Addad, Karthik C. S., David Saulpic, and Chris Schwiegelshohn

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

Abstract

The k-median and k-means clustering objectives are classic objectives for modeling clustering in a metric space. Given a set of points in a metric space, the goal of the k-median (resp. k-means) problem is to find k representative points so as to minimize the sum of the distances (resp. sum of squared distances) from each point to its closest representative. Cohen-Addad, Feldmann, and Saulpic [JACM'21] showed how to obtain a (1+ε)-factor approximation in low-dimensional Euclidean metric for both the k-median and k-means problems in near-linear time 2^{(1/ε)^O(d²)} n ⋅ polylog(n) (where d is the dimension and n is the number of input points). We improve this running time to 2^{O(1/ε)^{d-1}} ⋅ n ⋅ polylog(n), and show an almost matching lower bound: under the Gap Exponential Time Hypothesis for 3-SAT, there is no 2^o(1/ε^{d-1}) n^O(1) algorithm achieving a (1+ε)-approximation for k-means.

Cite as

Vincent Cohen-Addad, Karthik C. S., David Saulpic, and Chris Schwiegelshohn. Almost-Optimal Upper and Lower Bounds for Clustering in Low Dimensional Euclidean Spaces. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 34:1-34:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{cohenaddad_et_al:LIPIcs.SoCG.2026.34,
  author =	{Cohen-Addad, Vincent and Karthik C. S. and Saulpic, David and Schwiegelshohn, Chris},
  title =	{{Almost-Optimal Upper and Lower Bounds for Clustering in Low Dimensional Euclidean Spaces}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{34:1--34:16},
  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.2026.34},
  URN =		{urn:nbn:de:0030-drops-258404},
  doi =		{10.4230/LIPIcs.SoCG.2026.34},
  annote =	{Keywords: k-means clustering, k-median clustering, Euclidean space, Fine-Grained Complexity}
}

Document

DOI: 10.4230/LIPIcs.SoCG.2026.39

Locality Sensitive Hashing in Hyperbolic Space

Authors: Chengyuan Deng, Jie Gao, Kevin Lu, Feng Luo, and Cheng Xin

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

Abstract

For a metric space (X, d), a family ℋ of locality sensitive hash functions is called (r, cr, p₁, p₂) sensitive if a randomly chosen function h ∈ ℋ has probability at least p₁ (at most p₂) to map any a, b ∈ X in the same hash bucket if d(a, b) ≤ r (or d(a, b) ≥ cr). Locality Sensitive Hashing (LSH) is one of the most popular techniques for approximate nearest-neighbor search in high-dimensional spaces, and has been studied extensively for Hamming, Euclidean, and spherical geometries. An (r, cr, p₁, p₂)-sensitive hash function enables approximate nearest neighbor search (i.e., returning a point within distance cr from a query q if there exists a point within distance r from q) with space O(n^{1+ρ}) and query time O(n^ρ) where ρ = (log 1/p₁)/(log 1/p₂). But LSH for hyperbolic spaces ℍ^d remains largely unexplored. In this work, we present the first LSH construction native to hyperbolic space. For the hyperbolic plane (d = 2), we show a construction achieving ρ ≤ 1/c, based on the hyperplane rounding scheme. For general hyperbolic spaces (d ≥ 3), we use dimension reduction from ℍ^d to ℍ² and the 2D hyperbolic LSH to get ρ ≤ 1.59/c. On the lower bound side, we show that the lower bound on ρ of Euclidean LSH extends to the hyperbolic setting via local isometry, therefore giving ρ ≥ 1/c².

Cite as

Chengyuan Deng, Jie Gao, Kevin Lu, Feng Luo, and Cheng Xin. Locality Sensitive Hashing in Hyperbolic Space. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 39:1-39:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{deng_et_al:LIPIcs.SoCG.2026.39,
  author =	{Deng, Chengyuan and Gao, Jie and Lu, Kevin and Luo, Feng and Xin, Cheng},
  title =	{{Locality Sensitive Hashing in Hyperbolic Space}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{39:1--39:19},
  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.2026.39},
  URN =		{urn:nbn:de:0030-drops-258454},
  doi =		{10.4230/LIPIcs.SoCG.2026.39},
  annote =	{Keywords: Locality Sensitive Hashing, Hyperbolic Geometry, Dimension Reduction, Approximate Nearest Neighbor Search}
}

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.2026.54

Approximating Pareto Sum via Bounded Monotone Min-Plus Convolution

Authors: Geri Gokaj, Marvin Künnemann, Sabine Storandt, and Carina Truschel

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

Abstract

The Pareto sum of two-dimensional point sets P and Q in ℝ² is defined as the skyline of the points in their Minkowski sum. The problem of efficiently computing the Pareto sum arises frequently in bi-criteria optimization algorithms. Prior work establishes that computing the Pareto sum of sets P and Q of size n suffers from conditional lower bounds that rule out strongly subquadratic O(n^{2-ε})-time algorithms, even when the output size is Θ(n). Naturally, we ask: How efficiently can we approximate Pareto sums, both in theory and practice? Can we beat the near-quadratic-time state of the art for exact algorithms? On the theoretical side, we formulate a notion of additively approximate Pareto sets and show that computing an approximate Pareto set is fine-grained equivalent to Bounded Monotone Min-Plus Convolution. Leveraging a remarkable Õ(n^{1.5})-time algorithm for the latter problem (Chi, Duan, Xie, Zhang; STOC '22), we thus obtain a strongly subquadratic (and conditionally optimal) approximation algorithm for computing Pareto sums. On the practical side, we engineer different algorithmic approaches for approximating Pareto sets on realistic instances. Our implementations enable a granular trade-off between approximation quality and running time/output size compared to the state of the art for exact algorithms established in (Funke, Hespe, Sanders, Storandt, Truschel; Algorithmica '25). Perhaps surprisingly, the (theoretical) connection to Bounded Monotone Min-Plus Convolution remains beneficial even for our implementations: in particular, we implement a simplified, yet still subquadratic version of an algorithm due to Chi, Duan, Xie and Zhang, which on some sufficiently large instances outperforms the competing quadratic-time approaches.

Cite as

Geri Gokaj, Marvin Künnemann, Sabine Storandt, and Carina Truschel. Approximating Pareto Sum via Bounded Monotone Min-Plus Convolution. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 54:1-54:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{gokaj_et_al:LIPIcs.SoCG.2026.54,
  author =	{Gokaj, Geri and K\"{u}nnemann, Marvin and Storandt, Sabine and Truschel, Carina},
  title =	{{Approximating Pareto Sum via Bounded Monotone Min-Plus Convolution}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{54:1--54:21},
  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.2026.54},
  URN =		{urn:nbn:de:0030-drops-258602},
  doi =		{10.4230/LIPIcs.SoCG.2026.54},
  annote =	{Keywords: computational geometry, fine-grained complexity, algorithm engineering}
}

Document

DOI: 10.4230/LIPIcs.SoCG.2026.56

Optimal Randomized Clustering of Matrices

Authors: Mustafa Alper Gunes and Assaf Naor

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

Abstract

If X = (𝖬_n(ℝ),‖⋅‖_X) is a unitarily invariant normed space, i.e., ‖𝖴𝖠𝖵‖_X = ‖𝖠‖_X for every matrix 𝖠 ∈ 𝖬_n(ℝ) and every two orthogonal matrices 𝖴,𝖵 ∈ 𝖬_n(ℝ), then we evaluate up to universal constant factors the smallest σ > 0 for which there is a probability distribution over partitions of X into clusters of diameter at most 1 yet for every two matrices 𝖠,𝖡 ∈ 𝖬_n(ℝ) the probability that they fall into distinct clusters is at most σ times the X-distance between 𝖠 and 𝖡. Specifically, we prove that this infimal σ, which is called the separation modulus of X and is denoted SEP(X), satisfies: (1) SEP(X) = Θ(√n⋅ ‖𝖨_n‖_X⋅ diam(B_X)), where 𝖨_n is the n-by-n identity matrix and diam(B_X) is the diameter with respect to the standard Euclidean metric on 𝖬_n(ℝ) of the unit ball B_ X of X. Our proof of (1) proceeds through an asymptotic evaluation of the spectral gap of the Laplacian with Dirichlet boundary conditions on B_ X, which we achieve by exact computations for a Jacobi orthogonal random matrix ensemble. Assuming oracle access to norm evaluations in X, by combining (1) with a new deterministic algorithm for a O(1)-approximation of the diameter of convex bodies in ℝⁿ that are given by a weak membership oracle and are symmetric with respect to coordinate permutations and reflections about the standard axes (this task is famously known to be impossible in the absence of such symmetries), we get an oracle polynomial time algorithm whose output is the separation modulus of X up to universal constant factors. Another example of a consequence of (1) is that for each m ∈ {1,…,n} the separation modulus of the m'th Ky Fan norm on 𝖬_n(ℝ) is bounded from above and from below by universal constant multiples of m√n if m ⩾ √n, and of n if m ⩽ √n. We also deduce from (1) an upper bound on the Lipschitz extension modulus of X that improves over the previously best-known bound even in the special case when X is 𝖬_n(ℝ) equipped with the 𝓁₂ⁿ → 𝓁₂ⁿ operator norm.

Cite as

Mustafa Alper Gunes and Assaf Naor. Optimal Randomized Clustering of Matrices. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 56:1-56:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{gunes_et_al:LIPIcs.SoCG.2026.56,
  author =	{Gunes, Mustafa Alper and Naor, Assaf},
  title =	{{Optimal Randomized Clustering of Matrices}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{56:1--56:18},
  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.2026.56},
  URN =		{urn:nbn:de:0030-drops-258624},
  doi =		{10.4230/LIPIcs.SoCG.2026.56},
  annote =	{Keywords: Clustering, Unitarily Invariant Matrix Norms, Oracle Polynomial Time Approximation Algorithms for Radii of Convex Bodies, Extension of Lipschitz Functions, Random Matrices, Spectrum of the Laplacian with Dirichlet Boundary Conditions, Reverse Isoperimetry}
}

@InProceedings{gunes_et_al:LIPIcs.SoCG.2026.56,
  author =	{Gunes, Mustafa Alper and Naor, Assaf},
  title =	{{Optimal Randomized Clustering of Matrices}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{56:1--56:18},
  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.2026.56},
  URN =		{urn:nbn:de:0030-drops-258624},
  doi =		{10.4230/LIPIcs.SoCG.2026.56},
  annote =	{Keywords: Clustering, Unitarily Invariant Matrix Norms, Oracle Polynomial Time Approximation Algorithms for Radii of Convex Bodies, Extension of Lipschitz Functions, Random Matrices, Spectrum of the Laplacian with Dirichlet Boundary Conditions, Reverse Isoperimetry}
}

Document

DOI: 10.4230/LIPIcs.SoCG.2026.60

Space-Efficient Approximate Spherical Range Counting in High Dimensions

Authors: Andreas Kalavas and Ioannis Psarros

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

Abstract

We study the following range searching problem in high-dimensional Euclidean spaces: given a finite set P ⊂ ℝ^d, where each p ∈ P is assigned a weight w_p, and radius r > 0, we need to preprocess P into a data structure such that when a new query point q ∈ ℝ^d arrives, the data structure reports the cumulative weight of points of P within Euclidean distance r from q. Solving the problem exactly seems to require space usage that is exponential to the dimension, a phenomenon known as the curse of dimensionality. Thus, we focus on approximate solutions where points up to (1+ε)r away from q may be taken into account, where ε > 0 is an input parameter known during preprocessing. We build a data structure with near-linear space usage, and query time in n^{1-Θ(ε⁴/log(1/ε))}+t_q^ϱ⋅n^{1-ϱ}, for some ϱ = Θ(ε²), where t_q is the number of points of P in the ambiguity zone, i.e., at distance between r and (1+ε)r from the query q. To the best of our knowledge, this is the first data structure with efficient space usage (subquadratic or near-linear for any ε > 0) and query time that remains sublinear for any sublinear t_q. We supplement our worst-case bounds with a query-driven preprocessing algorithm to build data structures that are well-adapted to the query distribution.

Cite as

Andreas Kalavas and Ioannis Psarros. Space-Efficient Approximate Spherical Range Counting in High Dimensions. In 42nd International Symposium on Computational Geometry (SoCG 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 367, pp. 60:1-60:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{kalavas_et_al:LIPIcs.SoCG.2026.60,
  author =	{Kalavas, Andreas and Psarros, Ioannis},
  title =	{{Space-Efficient Approximate Spherical Range Counting in High Dimensions}},
  booktitle =	{42nd International Symposium on Computational Geometry (SoCG 2026)},
  pages =	{60:1--60:15},
  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.2026.60},
  URN =		{urn:nbn:de:0030-drops-258670},
  doi =		{10.4230/LIPIcs.SoCG.2026.60},
  annote =	{Keywords: Approximate range counting, partition trees, high dimensions}
}

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