Algebraic Combinatorics in Data Science and Optimisation

A special issue of AppliedMath (ISSN 2673-9909).

Deadline for manuscript submissions: 31 July 2025 | Viewed by 860

Special Issue Editor


E-Mail Website
Guest Editor
Department of Pure and Applied Mathematics, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita 565-0871, Osaka, Japan
Interests: computational commutative algebra; discrete mathematics; combinatorics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, data science and optimization are attractive fields. There is no doubt that algebraic and combinatorial methods are extremely useful in this research field. Combinatorics, on the other hand, is a broad field, and algebraic combinatorics in particular has made tremendous progress over the past quarter century and continues to develop as a research field that organically links combinatorics and various branches of algebra. This Special Issue aims to capture the shades of algebraic combinatorics that emerge in data science and optimization research to provide new topics in algebraic combinatorics from the perspective of applied mathematics, and to give birth to a novel trend in algebraic combinatorics.

Prof. Dr. Takayuki Hibi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AppliedMath is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • convex polytope
  • finite partially ordered set
  • finite graph
  • simplicial complex
  • statistics
  • experimental design
  • business acumen
  • communication
  • databases
  • marketing
  • discrete optimization
  • linear programming
  • integer programming
  • machine learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

21 pages, 581 KiB  
Article
The Search-o-Sort Theory
by Anurag Dutta, Sanjeev Kumar, Deepkiran Munjal and Pijush Kanti Kumar
AppliedMath 2025, 5(2), 64; https://doi.org/10.3390/appliedmath5020064 - 29 May 2025
Viewed by 126
Abstract
In the modern era of informatics, where data are very important, efficient management of data is necessary and critical. Two of the most important data management techniques are searching and data ordering (technically sorting). Traditional sorting algorithms work in quadratic time [...] Read more.
In the modern era of informatics, where data are very important, efficient management of data is necessary and critical. Two of the most important data management techniques are searching and data ordering (technically sorting). Traditional sorting algorithms work in quadratic time Ox2, and in the optimized cases, they take linearithmic time Ox·logx, with no existing method surpass this lower bound, given arbitrary data, i.e., ordering a list of cardinality x in Ox·logxϵ(x)ϵ(x)>0. This research proposes Search-o-Sort, which reinterprets sorting in terms of searching, thereby offering a new framework for ordering arbitrary data. The framework is applied to classical search algorithms,–Linear Search, Binary Search (in general, k-ary Search), and extended to more optimized methods such as Interpolation and Jump Search. The analysis suggests theoretical pathways to reduce the computational complexity of sorting algorithms, thus enabling algorithmic development based on the proposed viewpoint. Full article
(This article belongs to the Special Issue Algebraic Combinatorics in Data Science and Optimisation)
Show Figures

Figure 1

Back to TopTop