Selected Algorithmic Papers from IWOCA 2024

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (1 December 2024) | Viewed by 2942

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Department of Computer Science, University of Salerno, 84084 Fisciano, Salerno, Italy
Interests: design and analysis of algorithms; network algorithms; social network analysis; parameterized algorithms and complexity; combinatorial structures
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Special Issue Information

Dear Colleagues,

The 35th International Workshop on Combinatorial Algorithms IWOCA 2024 is an annual international conference held in Italy. IWOCA 2024 is designed to cover a broad range of topics in Algorithmics and Combinatorial Structures. Further details can be found here: http://iwoca2024.di.unisa.it/.

Several extended conference papers regarding algorithms will be invited to this Special Issue of the Algorithms journal to be published in open access form. The Special Issue is also open for papers not presented at the Workshop, whose topics fit that of IWOCA 2024.

Dr. Adele Anna Rescigno
Guest Editor

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Keywords

  • ad hoc, dynamic and evolving networks
  • algorithms and data structures
  • algorithms on strings and graphs
  • algorithms for big data and networks analytics
  • algorithmic game theory
  • approximation algorithms
  • circuits and boolean functions
  • combinatorial generation, enumeration, and counting
  • combinatorial optimization
  • complexity theory
  • combinatorics of words
  • computational algebra and geometry
  • computational biology
  • cryptography and information security
  • distributed and parallel algorithms
  • experimental evaluation of algorithms
  • fine-grained complexity
  • foundations of cloud computing
  • graph algorithms for social network analysis
  • graph drawing and labelling
  • graph theory and combinatorics
  • mobile agents
  • new paradigms of computation
  • online algorithms
  • parameterized and exact algorithms
  • probabilistic and randomized algorithms
  • scheduling
  • streaming algorithms

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Published Papers (2 papers)

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Research

12 pages, 535 KiB  
Article
Text Indexing for Faster Gapped Pattern Matching
by Md Helal Hossen, Daniel Gibney and Sharma V. Thankachan
Algorithms 2024, 17(12), 537; https://doi.org/10.3390/a17120537 - 23 Nov 2024
Viewed by 319
Abstract
We revisit the following version of the Gapped String Indexing problem, where the goal is to preprocess a text T[1..n] to enable efficient reporting of all occ occurrences of a gapped pattern [...] Read more.
We revisit the following version of the Gapped String Indexing problem, where the goal is to preprocess a text T[1..n] to enable efficient reporting of all occ occurrences of a gapped pattern P=P1[α..β]P2 in T. An occurrence of P in T is defined as a pair (i,j) where substrings T[i..i+|P1|) and T[j..j+|P2|) match P1 and P2, respectively, with a gap j(i+|P1|) lying within the interval [α..β]. This problem has significant applications in computational biology and text mining. A hardness result on this problem suggests that any index with polylogarithmic query time must occupy near quadratic space. In a recent study [STACS 2024], Bille et al. presented a sub-quadratic space index using space O˜(n2δ/3), where 0δ1 is a parameter fixed at the time of index construction. Its query time is O˜(|P1|+|P2|+nδ·(1+occ)), which is sub-linear per occurrence when δ<1. We show how to achieve a gap-sensitive query time of O˜(|P1|+|P2|+nδ·(1+occ1δ)+g[α..β]occg·gδ) using the same space, where occg denotes the number of occurrences with gap g. This is faster when there are many occurrences with small gaps. Full article
(This article belongs to the Special Issue Selected Algorithmic Papers from IWOCA 2024)
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11 pages, 250 KiB  
Article
Hardness and Approximability of Dimension Reduction on the Probability Simplex
by Roberto Bruno
Algorithms 2024, 17(7), 296; https://doi.org/10.3390/a17070296 - 6 Jul 2024
Viewed by 1494
Abstract
Dimension reduction is a technique used to transform data from a high-dimensional space into a lower-dimensional space, aiming to retain as much of the original information as possible. This approach is crucial in many disciplines like engineering, biology, astronomy, and economics. In this [...] Read more.
Dimension reduction is a technique used to transform data from a high-dimensional space into a lower-dimensional space, aiming to retain as much of the original information as possible. This approach is crucial in many disciplines like engineering, biology, astronomy, and economics. In this paper, we consider the following dimensionality reduction instance: Given an n-dimensional probability distribution p and an integer m<n, we aim to find the m-dimensional probability distribution q that is the closest to p, using the Kullback–Leibler divergence as the measure of closeness. We prove that the problem is strongly NP-hard, and we present an approximation algorithm for it. Full article
(This article belongs to the Special Issue Selected Algorithmic Papers from IWOCA 2024)
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