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DOI: 10.4230/LIPIcs.FSTTCS.2024.11

Many Flavors of Edit Distance

Authors: Sudatta Bhattacharya, Sanjana Dey, Elazar Goldenberg, and Michal Koucký

Published in: LIPIcs, Volume 323, 44th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2024)

Abstract

Several measures exist for string similarity, including notable ones like the edit distance and the indel distance. The former measures the count of insertions, deletions, and substitutions required to transform one string into another, while the latter specifically quantifies the number of insertions and deletions. Many algorithmic solutions explicitly address one of these measures, and frequently techniques applicable to one can also be adapted to work with the other. In this paper, we investigate whether there exists a standardized approach for applying results from one setting to another. Specifically, we demonstrate the capability to reduce questions regarding string similarity over arbitrary alphabets to equivalent questions over a binary alphabet. Furthermore, we illustrate how to transform questions concerning indel distance into equivalent questions based on edit distance. This complements an earlier result of Tiskin (2007) which addresses the inverse direction.

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Sudatta Bhattacharya, Sanjana Dey, Elazar Goldenberg, and Michal Koucký. Many Flavors of Edit Distance. In 44th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 323, pp. 11:1-11:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{bhattacharya_et_al:LIPIcs.FSTTCS.2024.11,
  author =	{Bhattacharya, Sudatta and Dey, Sanjana and Goldenberg, Elazar and Kouck\'{y}, Michal},
  title =	{{Many Flavors of Edit Distance}},
  booktitle =	{44th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2024)},
  pages =	{11:1--11:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-355-3},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{323},
  editor =	{Barman, Siddharth and Lasota, S{\l}awomir},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2024.11},
  URN =		{urn:nbn:de:0030-drops-222004},
  doi =		{10.4230/LIPIcs.FSTTCS.2024.11},
  annote =	{Keywords: Edit distance, Indel distance, Embedding, LCS, Alphabet Reduction}
}

Document

DOI: 10.4230/LIPIcs.ISAAC.2023.17

Matrix Completion: Approximating the Minimum Diameter

Authors: Diptarka Chakraborty and Sanjana Dey

Published in: LIPIcs, Volume 283, 34th International Symposium on Algorithms and Computation (ISAAC 2023)

Abstract

In this paper, we focus on the matrix completion problem and aim to minimize the diameter over an arbitrary alphabet. Given a matrix M with missing entries, our objective is to complete the matrix by filling in the missing entries in a way that minimizes the maximum (Hamming) distance between any pair of rows in the completed matrix (also known as the diameter of the matrix). It is worth noting that this problem is already known to be NP-hard. Currently, the best-known upper bound is a 4-approximation algorithm derived by applying the triangle inequality together with a well-known 2-approximation algorithm for the radius minimization variant. In this work, we make the following contributions: - We present a novel 3-approximation algorithm for the diameter minimization variant of the matrix completion problem. To the best of our knowledge, this is the first approximation result that breaks below the straightforward 4-factor bound. - Furthermore, we establish that the diameter minimization variant of the matrix completion problem is (2-ε)-inapproximable, for any ε > 0, even when considering a binary alphabet, under the assumption that 𝖯 ≠ NP. This is the first result that demonstrates a hardness of approximation for this problem.

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Diptarka Chakraborty and Sanjana Dey. Matrix Completion: Approximating the Minimum Diameter. In 34th International Symposium on Algorithms and Computation (ISAAC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 283, pp. 17:1-17:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{chakraborty_et_al:LIPIcs.ISAAC.2023.17,
  author =	{Chakraborty, Diptarka and Dey, Sanjana},
  title =	{{Matrix Completion: Approximating the Minimum Diameter}},
  booktitle =	{34th International Symposium on Algorithms and Computation (ISAAC 2023)},
  pages =	{17:1--17:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-289-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{283},
  editor =	{Iwata, Satoru and Kakimura, Naonori},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2023.17},
  URN =		{urn:nbn:de:0030-drops-193197},
  doi =		{10.4230/LIPIcs.ISAAC.2023.17},
  annote =	{Keywords: Incomplete Data, Matrix Completion, Hamming Distance, Diameter Minimization, Approximation Algorithms, Hardness of Approximation}
}

Document

DOI: 10.4230/LIPIcs.ISAAC.2020.24

Discriminating Codes in Geometric Setups

Authors: Sanjana Dey, Florent Foucaud, Subhas C. Nandy, and Arunabha Sen

Published in: LIPIcs, Volume 181, 31st International Symposium on Algorithms and Computation (ISAAC 2020)

Abstract

We study two geometric variations of the discriminating code problem. In the discrete version, a finite set of points P and a finite set of objects S are given in ℝ^d. The objective is to choose a subset S^* ⊆ S of minimum cardinality such that the subsets S_i^* ⊆ S^* covering p_i, satisfy S_i^* ≠ ∅ for each i = 1,2,…, n, and S_i^* ≠ S_j^* for each pair (i,j), i ≠ j. In the continuous version, the solution set S^* can be chosen freely among a (potentially infinite) class of allowed geometric objects. In the 1-dimensional case (d = 1), the points are placed on some fixed-line L, and the objects in S are finite segments of L (called intervals). We show that the discrete version of this problem is NP-complete. This is somewhat surprising as the continuous version is known to be polynomial-time solvable. This is also in contrast with most geometric covering problems, which are usually polynomial-time solvable in 1D. We then design a polynomial-time 2-approximation algorithm for the 1-dimensional discrete case. We also design a PTAS for both discrete and continuous cases when the intervals are all required to have the same length. We then study the 2-dimensional case (d = 2) for axis-parallel unit square objects. We show that both continuous and discrete versions are NP-hard, and design polynomial-time approximation algorithms with factors 4+ε and 32+ε, respectively (for every fixed ε > 0).

Cite as

Sanjana Dey, Florent Foucaud, Subhas C. Nandy, and Arunabha Sen. Discriminating Codes in Geometric Setups. In 31st International Symposium on Algorithms and Computation (ISAAC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 181, pp. 24:1-24:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{dey_et_al:LIPIcs.ISAAC.2020.24,
  author =	{Dey, Sanjana and Foucaud, Florent and Nandy, Subhas C. and Sen, Arunabha},
  title =	{{Discriminating Codes in Geometric Setups}},
  booktitle =	{31st International Symposium on Algorithms and Computation (ISAAC 2020)},
  pages =	{24:1--24:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-173-3},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{181},
  editor =	{Cao, Yixin and Cheng, Siu-Wing and Li, Minming},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2020.24},
  URN =		{urn:nbn:de:0030-drops-133686},
  doi =		{10.4230/LIPIcs.ISAAC.2020.24},
  annote =	{Keywords: Discriminating code, Approximation algorithm, Segment stabbing, Geometric Hitting set}
}

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Documents authored by Dey, Sanjana

Many Flavors of Edit Distance

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Matrix Completion: Approximating the Minimum Diameter

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Discriminating Codes in Geometric Setups

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