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Symmetry
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Asymmetric and Symmetric Study on Algorithms Optimization
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Asymmetric and Symmetric Study on Algorithms Optimization

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 4512

Special Issue Editors

Prof. Dr. Marcio Basilio

E-Mail Website
Guest Editor
Department of Production Engineering, Faculty of Engineering, University Federal Fluminense, Niteroi, Brazil
Interests: operational research; multicriteria; ELECTRE I, III, IV; PROMETHEE; latent dirichlet allocation; text mining; topic model; human resources; police; police education; public security
Special Issues, Collections and Topics in MDPI journals
Dr. Valdecy Pereira

E-Mail Website
Guest Editor
Department of Production Engineering, Faculty of Engineering, University Federal Fluminense, Niteroi, Brazil
Interests: decision making; ELECTRE method; multiple criteria

Special Issue Information

Dear Colleagues,

Asymmetric Study:

In asymmetric algorithm optimization, the focus is on leveraging the unique strengths or characteristics of the different components or entities involved. This approach aims to maximize optimization by exploiting specific advantages of individual elements rather than trying to achieve balance across all components. Asymmetric study involves identifying and utilizing the specific capabilities or resources of each entity to enhance overall performance. This approach is suitable for problems where each entity has different characteristics or capabilities that can be harnessed for optimized solutions.

Symmetric Study:

Symmetric algorithm optimization focuses on achieving balanced or equally efficient solutions across multiple components or entities. The aim is to distribute resources, workloads, or computational tasks evenly to ensure fairness, equal processing time, and optimal utilization of resources. Techniques such as load balancing, parallel computing, and task scheduling are often used in symmetric study to achieve optimal performance across all entities involved. This approach is appropriate when the components or entities are homogeneous and do not possess distinct advantages or differences in capabilities.

In both asymmetric and symmetric algorithm optimization, the choice depends on the specific problem, the characteristics of the entities involved, and the desired outcome. By understanding these approaches, researchers and practitioners can select the most suitable strategy to achieve efficient and optimized algorithms.

Prof. Dr. Marcio Basilio
Dr. Valdecy Pereira
Guest Editors

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. Symmetry is an international peer-reviewed open access monthly 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 2400 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

  • symmetric algorithms
  • balanced optimization
  • equally efficient solutions
  • resource distribution
  • fairness
  • parallel computing
  • load balancing
  • task scheduling
  • homogeneous components
  • optimal performance

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Published Papers (2 papers)

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39 pages, 8597 KiB  
Article
Multilevel Algorithm for Large-Scale Gravity Inversion
by Shujin Cao, Peng Chen, Guangyin Lu, Yajing Mao, Dongxin Zhang, Yihuai Deng and Xinyue Chen
Symmetry 2024, 16(6), 758; https://doi.org/10.3390/sym16060758 - 17 Jun 2024
Viewed by 1273
Abstract
Surface gravity inversion attempts to recover the density contrast distribution in the 3D Earth model for geological interpretation. Since airborne gravity is characterized by large data volumes, large-scale 3D inversion exceeds the capacity of desktop computing resources, making it difficult to achieve the [...] Read more.
Surface gravity inversion attempts to recover the density contrast distribution in the 3D Earth model for geological interpretation. Since airborne gravity is characterized by large data volumes, large-scale 3D inversion exceeds the capacity of desktop computing resources, making it difficult to achieve the appropriate depth/lateral resolution for geological interpretation. In addition, gravity data are finite and noisy, and their inversion is ill posed. Especially in the absence of a priori geological information, regularization must be introduced to overcome the difficulty of the non-uniqueness of the solutions to recover the most geologically plausible ones. Because the use of Haar wavelet operators has an edge-preserving property and can preserve the sensitivity matrix structure at each level of the multilevel method to obtain faster solvers, we present a multilevel algorithm for large-scale gravity inversion solved by the re-weighted regularized conjugate gradient (RRCG) algorithm to reduce the inversion computational resources and improve the depth/lateral resolution of the inversion results. The RRCG-based multilevel inversion was then applied to synthetic cases and airborne gravity data from the Quest-South project in British Columbia, Canada. Results from synthetic models and field data show that the RRCG-based multilevel inversion is suitable for obtaining density contrast distributions with appropriate horizontal and vertical resolution, especially for large-scale gravity inversions compared to Occam’s inversion. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Algorithms Optimization)
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26 pages, 3314 KiB  
Article
Lightweight Computational Complexity Stepping Up the NTRU Post-Quantum Algorithm Using Parallel Computing
by Ghada Farouk Elkabbany, Hassan I. Sayed Ahmed, Heba K. Aslan, Young-Im Cho and Mohamed S. Abdallah
Symmetry 2024, 16(1), 12; https://doi.org/10.3390/sym16010012 - 21 Dec 2023
Viewed by 2154
Abstract
The Nth-degree Truncated polynomial Ring Unit (NTRU) is one of the famous post-quantum cryptographic algorithms. Researchers consider NTRU to be the most important parameterized family of lattice-based public key cryptosystems that has been established to the IEEE P1363 standards. Lattice-based protocols necessitate operations [...] Read more.
The Nth-degree Truncated polynomial Ring Unit (NTRU) is one of the famous post-quantum cryptographic algorithms. Researchers consider NTRU to be the most important parameterized family of lattice-based public key cryptosystems that has been established to the IEEE P1363 standards. Lattice-based protocols necessitate operations on large vectors, which makes parallel computing one of the appropriate solutions to speed it up. NTRUEncrypt operations contain a large amount of data that requires many repetitive arithmetic operations. These operations make it a strong candidate to take advantage of the high degree of parallelism. The main costly operation that is repeated in all NTRU algorithm steps is polynomial multiplication. In this work, a Parallel Post-Quantum NTRUEncrypt algorithm called PPQNTRUEncrypt is proposed. This algorithm exploits the capabilities of parallel computing to accelerate the NTRUEncrypt algorithm. Both analytical and Apache Spark simulation models are used. The proposed algorithm enhanced the NTRUEncrypt algorithm by approximately 49.5%, 74.5%, 87.6%, 92.5%, 93.4%, and 94.5%, assuming that the number of processing elements is 2, 4, 8, 12, 16, and 20 respectively. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Algorithms Optimization)
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