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Science Talks 2025年 13卷

作者： Khattak, Faizan A. Proudler, Ian K. Weiss, Stephan Dept. of Electronic & Electrical Eng University of Strathclyde Scotland Glasgow United Kingdom

In order to extract the analytic eigenvalues from a parahermitian matrix, the computational cost of the current state-of-the-art method grows factorially with the matrix dimension. Even though the approach offers benefits such as proven convergence, it has been found impractical to operate on matrices with a spatial dimension great than four. Evaluated in the discrete Fourier transform (DFT) domain, the computational bottleneck of this method is a maximum likelihood sequence (MLS) estimation, which probes a set of paths of likely associations across DFT bins, and only retains the best of these. In this paper, we investigate an algorithm that remains covered by the existing method's proof of convergence but results in a significant reduction in computation cost by trading the number of retained paths against the DFT length. We motivate this, and also introduce an enhanced initialisation point for the MLS estimation. We illustrate the benefits of scalable analytic extraction algorithm in a number of simulations. © 2025 The Authors

关键词： algorithm scalability Analytic eigenvalue decomposition Computational cost Maximum likelihood sequence estimation Space-time covariance

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A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年第19期35卷 14275-14378页

作者： Turgut, Oguz Emrah Turgut, Mert Sinan Kirtepe, Erhan Izmir Bakircay Univ Fac Engn & Architecture Dept Ind Engn Menemen Izmir Turkiye Ege Univ Fac Engn Dept Mech Engn Bornova Izmir Turkiye Sirnak Univ Dept Motor Vehicles & Transportat Technol Sirnak Turkiye

The scientific field of optimization has witnessed an increasing trend in the development of metaheuristic algorithms within the current decade. The vast majority of the proposed algorithms have been proclaimed as superior and highly efficient compared to their contemporary counterparts by their own developers, which should be verified on a set of benchmark cases if it is to give conducive insights into their true capabilities. This study completes a comprehensive investigation of the general optimization capabilities of the recently developed nature-inspired metaheuristic algorithms, which have not been thoroughly discussed in past literature studies due to their new emergence. To overcome this deficiency in the existing literature, optimization benchmark problems with different functional characteristics will be solved by some of the widely used recent optimizers. Unconstrained standard test functions comprised of thirty-four unimodal scalable optimization problems with varying dimensionalities have been solved by these competitive algorithms, and respective estimated solutions have been evaluated relying on the performance metrics defined by the statistical analysis of the predictive results. Convergence curves of the algorithms have been construed to observe the evolution trends of objective function values. To further delve into comprehensive analysis on unconstrained test cases, CEC 2013 problems have been considered for comparison tools since their resemblances of the following features of real-world complex algorithms. The optimization capabilities of eleven metaheuristics algorithms have been comparatively analyzed on twenty-eight multidimensional problems. Finally, fourteen complex engineering problems have been optimized by the algorithms to scrutinize their effectiveness on handling the imposed design constraints.

关键词： algorithm comparison algorithm scalability Metaheuristic algorithms Real-world design problems

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MCGS: A modified conjugate gradient squared algorithm for nonsymmetric linear systems

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JOURNAL OF SUPERCOMPUTING 1999年第3期14卷 257-280页

作者： Maheswaran, M Webb, KJ Siegel, HJ Univ Manitoba Dept Comp Sci Winnipeg MB R3T 2N2 Canada Purdue Univ Sch Elect & Comp Engn Parallel Proc Lab W Lafayette IN 47907 USA

The conjugate gradient squared (CGS) algorithm is a Krylov subspace algorithm that can be used to obtain fast solutions for linear systems (Ax=b) with complex nonsymmetric, very large, and very sparse coefficient matrices (A). By considering electromagnetic scattering problems as examples, a study of the performance and scalability of this algorithm on two MIMD machines is presented. A modified CGS (MCGS) algorithm, where the synchronization overhead is effectively reduced by a factor of two, is proposed in this paper. This is achieved by changing the computation sequence in the CGS algorithm. Both experimental and theoretical analyses are performed to investigate the impact of this modification on the overall execution time. From the theoretical and experimental analysis it is found that CGS is faster than MCGS for smaller number of processors and MCGS outperforms CGS as the number of processors increases. Based on this observation, a set of algorithms approach is proposed, where either CGS or MGS is selected depending on the values of the dimension of the A matrix (N) and number of processors (P). The set approach provides an algorithm that is more scalable than either the CGS or MCGS algorithms. The experiments performed on a 128-processor mesh Intel Paragon and on a 16-processor IBM SP2 with multistage network indicate that MCGS is approximately 20% faster than CGS.

关键词： algorithm scalability conjugate gradient squared modified conjugate gradient squared Intel Paragon IBM SP-2 MIMD synchronization

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Using bus linearization to scale the reconfigurable mesh

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2002年第4期62卷 495-516页

作者： Fernández-Zepeda, JA Vaidyanathan, R Trahan, JL Ctr Sci Res & Higher Educ Ensenada Dept Comp Sci Ensenada 22860 Baja California Mexico Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA

The reconfigurable mesh (R-Mesh) has drawn much interest in recent years due, in part, to its ability to admit extremely fast algorithms for a large number of problems. The unrestricted R-Mesh creates a great variety of bus shapes that facilitate algorithm design and reduce running time. In this paper, we present a bus linearization procedure that transforms an arbitrary non-linear bus configuration of an R-Mesh into an equivalent acyclic linear bus configuration implementable on a linear R-Mesh (LR-Mesh), a weaker version of the unrestricted R-Mesh. This procedure gives an algorithm designer the liberty of using buses of arbitrary shape, while automatically translating the algorithm to run on a simpler platform. We illustrate our bus linearization method through two important applications. The first leads to a faster scaling simulation for the unrestricted R-Mesh. This scaling simulation has an overhead of log N (smaller than the log P log N/P overhead of the previous fastest scaling simulation) and uses an exclusive write LR-Mesh as the simulating model;prior simulations needed concurrent write. The second application adapts algorithms designed for R-Meshes to run on models with pipelined optical buses. (C) 2002 Elsevier Science (USA).

关键词： bus linearization reconfigurable architectures and algorithms reconfigurable mesh algorithm scalability concurrent write rules pipelined optical bus

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New applications of ensembles of classifiers

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PATTERN ANALYSIS AND APPLICATIONS 2003年第3期6卷 245-256页

作者： Barandela, R Sánchez, JS Valdovinos, RM Inst Tecnol Toluca Metepec Mexico Univ Jaume 1 Castellon de La Plana Spain

Combination (ensembles) of classifiers is now a well established research line. It has been observed that the predictive accuracy of a combination of independent classifiers excels that of the single best classifier. While ensembles of classifiers have been mostly employed to achieve higher recognition accuracy, this paper focuses on the use of combinations of individual classifiers for handling several problems from the practice in the machine learning, pattern recognition and data mining domains. In particular, the study presented concentrates on managing the imbalanced training sample problem, scaling up some preprocessing algorithms and filtering the training set. Here, all these situations are examined mainly in connection with the nearest neighbour classifier. Experimental results show the potential of multiple classifier systems when applied to those situations.

关键词： algorithm scalability ensembles, filtering outliers imbalanced training sample nearest neighbour rule preprocessing techniques

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Rete-netzwerk-red: analyzing and visualizing scholarly networks using the Network Workbench Tool

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SCIENTOMETRICS 2010年第3期83卷 863-876页

作者： Boerner, Katy Huang, Weixia Linnemeier, Micah Duhon, Russell J. Phillips, Patrick Ma, Nianli Zoss, Angela M. Guo, Hanning Price, Mark A. Indiana Univ Sch Lib & Informat Sci Cyberinfrastruct Network Sci Ctr Bloomington IN 47401 USA

The enormous increase in digital scholarly data and computing power combined with recent advances in text mining, linguistics, network science, and scientometrics make it possible to scientifically study the structure and evolution of science on a large scale. This paper discusses the challenges of this 'BIG science of science'aEuro"also called 'computational scientometrics' research-in terms of data access, algorithm scalability, repeatability, as well as result communication and interpretation. It then introduces two infrastructures: (1) the Scholarly Database (SDB) ( http://*** ), which provides free online access to 22 million scholarly records-papers, patents, and funding awards which can be cross-searched and downloaded as dumps, and (2) Scientometrics-relevant plug-ins of the open-source Network Workbench (NWB) Tool ( http://*** ). The utility of these infrastructures is then exemplarily demonstrated in three studies: a comparison of the funding portfolios and co-investigator networks of different universities, an examination of paper-citation and co-author networks of major network science researchers, and an analysis of topic bursts in streams of text. The article concludes with a discussion of related work that aims to provide practically useful and theoretically grounded cyberinfrastructure in support of computational scientometrics research, education and practice.

关键词： Scientometrics Science of science Evolution of science Computational scientometrics Data access algorithm scalability Cyberinfrastructure Scholarly Database Network Workbench Related tools Open source Open access

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Novel Navigation algorithm for Wireless Sensor Networks Without Information of Locations

Novel Navigation Algorithm for Wireless Sensor Networks With...

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54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Guo, Peng Jiang, Tao Yi, Youwen Zhang, Qian Zhang, Kui Huazhong Univ Sci & Technol Wuhan Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Twente Enschede Netherlands

ISBN: (纸本)9781424492688

In this paper, we propose a novel distributed navigation algorithm for people to escape from critical event region in wireless sensor networks (WSNs). Unlike existing works, the scenario discussed in the paper has no goal or exit as guidance, leading to a big challenge for the navigation problem. To solve it, our proposed navigation algorithm computes the convex hull of the event region just by some topological methods. With the reference of the convex hull, people can be easily navigated out of the event region. Both the computation complexity and communication overhead of the proposed algorithm are very low, as it only needs to flood two shortest path trees in a limited area around the event region with a distance [L/2 pi] + 1 to the event boundary, where L is the length of the boundary. Conducted simulations have verified the effectiveness and scalability of the proposed algorithm.

关键词： Complexity theory IEEE Communications Society Logic gates NIST Navigation Peer to peer computing WSN Wireless sensor networks algorithm scalability communication complexity communication overhead computational complexity convex hull critical event region distributed navigation algorithm event boundary radionavigation shortest path trees telecommunication network topology topological methods trees (mathematics) wireless sensor networks wireless sensor networks

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Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering 23

Scalable Approximate NonSymmetric Autoencoder for Collaborat...

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17th ACM Conference on Recommender Systems (RecSys)

作者： Spisak, Martin Bartyzal, Radek Hoskovec, Antonin Peska, Ladislav Tuma, Miroslav GLAMI Prague Czech Republic Charles Univ Prague Fac Math & Phys Prague Czech Republic Czech Tech Univ Dept Phys Fac Nucl Sci & Phys Engn Prague Czech Republic

ISBN: (纸本)9798400702419

In the field of recommender systems, shallow autoencoders have recently gained significant attention. One of the most highly acclaimed shallow autoencoders is EASE(R), favored for its competitive recommendation accuracy and simultaneous simplicity. However, the poor scalability of EASE(R) (both in time and especially in memory) severely restricts its use in production environments with vast item sets. In this paper, we propose a hyperefficient factorization technique for sparse approximate inversion of the data-Gram matrix used in EASE(R). The resulting autoencoder, SANSA, is an end-to-end sparse solution with prescribable density and almost arbitrarily low memory requirements - even for training. As such, SANSA allows us to effortlessly scale the concept of EASE(R) to millions of items and beyond.

关键词： Sparse autoencoders Numerical approximation Sparse approximate inverse algorithm scalability

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Efficient Computation of Importance-Based Communities in Web-Scale Networks Using a Single Machine 16

Efficient Computation of Importance-Based Communities in Web...

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25th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Chen, Shu Wei, Ran Popova, Diana Thomo, Alex Univ Victoria Victoria BC V8W 2Y2 Canada

ISBN: (纸本)9781450340731

Finding decompositions of a graph into a family of communities is crucial to understanding its underlying structure. algorithms for finding communities in networks often rely only on structural information and search for cohesive subsets of nodes. In practice however, we would like to find communities that are not only cohesive, but also in influential or important. In order to capture such communities, Li, Qin, Yu, and Mao introduced a novel community model called "k-influential community" based on the concept of k-core, with numerical values representing "influence" assigned to the nodes. They formulate the problem of finding the top-r most important communities as finding r connected k-core subgraphs ordered by the lower-bound of their importance. In this paper, our goal is to scale-up the computation of top-r, k-core communities to web-scale graphs of tens of billions of edges. We feature several fast new algorithms for this problem. With our implementations, we show that we can efficiently handle massive networks using a single consumer-level machine within a reasonable amount of time.

关键词： graph compression community discovery algorithm scalability big graphs

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Lattice QCD on the IBM scalable POWERParallel systems SP2

Lattice QCD on the IBM scalable POWERParallel systems SP2

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1995 ACM/IEEE Supercomputing Conference (SC 95)

作者： Bernard, C DeTar, C Gottlieb, S Heller, UM Hetrick, J Ishizuka, N Karkkainen, L Lantz, SR Rummukainen, K Sugar, R Toussaint, D Wingate, M Washington Univ St. Louis United States

ISBN: (纸本)0897918622

A 512 node IBM Scalable POWERParallel Systems SP2 was installed at the Cornell Theory Center in October 1994. During the past couple of months we have Seen porting and optimizing code for carrying out lattice QCD calculations. Present performance is far from ideal, however, and optimization efforts are still under way. The rate limiting step in our code involves a rather generic inversion of. a large, sparse system, based on a partial differential equation in a multidimensional space. The insights we have gained so far may be useful in diagnosing performance in a wide class of applications. Copyright 1995 by the Association for Computing Machinery, Inc. (ACM).

关键词： IBM SP2 massively parallel processing lattice quantum chromodynamics MPL message passing algorithm optimization algorithm performance algorithm scalability partial differential equations sparse matrix problems Cornell Theory Center

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