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Готові списки джерел за темами / Matrix Reordering Techniques / Статті в журналах

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Статті в журналах з теми "Matrix Reordering Techniques"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Pichel, Juan C., Francisco F. Rivera, Marcos Fernández, and Aurelio Rodríguez. "Optimization of sparse matrix–vector multiplication using reordering techniques on GPUs." Microprocessors and Microsystems 36, no. 2 (2012): 65–77. http://dx.doi.org/10.1016/j.micpro.2011.05.005.

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2

LIU, YING, and WENYUAN LI. "VISUALIZING MICROARRAY DATA FOR BIOMARKER DISCOVERY BY MATRIX REORDERING AND REPLICATOR DYNAMICS." Journal of Bioinformatics and Computational Biology 06, no. 06 (2008): 1089–113. http://dx.doi.org/10.1142/s0219720008003862.

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In most microarray data sets, there are often multiple sample classes, which are categorized into the normal or diseased type. Traditional feature selection methods consider multiple classes equally without paying attention to the upregulation/downregulation across the normal and diseased classes; while the specific gene selection methods for biomarker discovery particularly consider differential gene expressions across the normal and diseased classes, but ignore the existence of multiple classes. More importantly, there are few visualization algorithms to assist biomarker discovery from micro

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3

Delis, Anargiros I., Maria Kazolea, and Maria Gaitani. "On the Numerical Solution of Sparse Linear Systems Emerging in Finite Volume Discretizations of 2D Boussinesq-Type Models on Unstructured Grids." Water 14, no. 21 (2022): 3584. http://dx.doi.org/10.3390/w14213584.

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This work aims to supplement the realization and validation of a higher-order well-balanced unstructured finite volume (FV) scheme, that has been relatively recently presented, for numerically simulating weakly non-linear weakly dispersive water waves over varying bathymetries. We investigate and develop solution strategies for the sparse linear system that appears during this FV discretisation of a set of extended Boussinesq-type equations on unstructured meshes. The resultant linear system of equations must be solved at each discrete time step as to recover the actual velocity field of the f

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4

Freire, Manuel, Raul Marichal, Agustin Martinez, Daniel Padron, Ernesto Dufrechou, and Pablo Ezzatti. "Leveraging index compression techniques to optimize the use of co-processors." Journal of Computer Science and Technology 24, no. 1 (2024): e01. http://dx.doi.org/10.24215/16666038.24.e01.

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   The significant presence that many-core devices like GPUs have these days, and their enormous computational power, motivates the study of sparse matrix operations in this hardware. The essential sparse kernels in scientific computing, such as the sparse matrix-vector multiplication (SpMV), usually have many different high-performance GPU implementations. Sparse matrix problems typically imply memory-bound operations, and this characteristic is particularly limiting in massively parallel processors. This work revisits the main ideas about reducing the volume of data require

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5

Ferragina, Paolo, Giovanni Manzini, Travis Gagie, et al. "Improving matrix-vector multiplication via lossless grammar-compressed matrices." Proceedings of the VLDB Endowment 15, no. 10 (2022): 2175–87. http://dx.doi.org/10.14778/3547305.3547321.

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As nowadays Machine Learning (ML) techniques are generating huge data collections, the problem of how to efficiently engineer their storage and operations is becoming of paramount importance. In this article we propose a new lossless compression scheme for real-valued matrices which achieves efficient performance in terms of compression ratio and time for linear-algebra operations. Experiments show that, as a compressor, our tool is clearly superior to gzip and it is usually within 20% of xz in terms of compression ratio. In addition, our compressed format supports matrix-vector multiplication

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6

Ndayikengurutse, Adrien, and Siming Huang. "Implementation of Presolving and Interior-Point Algorithm for Linear & Mixed Integer Programming: SOFTWARE." Journal of Systems Science and Information 8, no. 3 (2020): 195–223. http://dx.doi.org/10.21078/jssi-2020-195-29.

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AbstractLinear and mixed integer programming are very popular and important methods to make efficient scientific management decision. With large size of real application data, the use of linear-mixed integer programming is facing problems with more complexity; therefore, preprocessing techniques become very important. Preprocessing aims to check and delete redundant information from the problem formulation. It is a collection of techniques that reduce the size of the problem and try to strengthen the formulation. Fast and effective preprocessing techniques are very important and essential for

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7

Borst, Alexander, and Christian Leibold. "Connecting Connectomes to Physiology." Journal of Neuroscience 43, no. 20 (2023): 3599–610. http://dx.doi.org/10.1523/jneurosci.2208-22.2023.

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With the advent of volumetric EM techniques, large connectomic datasets are being created, providing neuroscience researchers with knowledge about the full connectivity of neural circuits under study. This allows for numerical simulation of detailed, biophysical models of each neuron participating in the circuit. However, these models typically include a large number of parameters, and insight into which of these are essential for circuit function is not readily obtained. Here, we review two mathematical strategies for gaining insight into connectomics data: linear dynamical systems analysis a

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8

Shen, Zhao-Li, Yu-Tong Liu, Bruno Carpentieri, Chun Wen, and Jian-Jun Wang. "Recursive reordering and elimination method for efficient computation of PageRank problems." AIMS Mathematics 8, no. 10 (2023): 25104–30. http://dx.doi.org/10.3934/math.20231282.

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<abstract><p>The PageRank model is widely utilized for analyzing a variety of scientific issues beyond its original application in modeling web search engines. In recent years, considerable research effort has focused on developing high-performance iterative methods to solve this model, particularly when the dimension is exceedingly large. However, due to the ever-increasing extent and size of data networks in various applications, the computational requirements of the PageRank model continue to grow. This has led to the development of new techniques that aim to reduce the computat

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9

Alexis, Kyriacou, Timotheou Stelios, Michaelides Michalis, Panayiotou Christos, and Polycarpou Marios. "Partitioning of Intelligent Buildings for Distributed Contaminant Detection and Isolation." IEEE Transactions on Emerging Topics in Computational Intelligence 1, no. 2 (2017): 72–86. https://doi.org/10.5281/zenodo.1012286.

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Intelligent buildings are responsible for ensuring indoor air quality for their occupants under normal operation as well as under possibly harmful contaminant events. An emerging environmental application involves the monitoring of intelligent buildings against harmful events by incorporating various sensing technologies and using sophisticated algorithms to detect and isolate such events. In this context, both centralized and distributed approaches have been proposed, with the latter having significant benefits in terms of complexity, scalability, reliabilit

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10

Byeon, Yeong-Hyeon, Jae-Neung Lee, Sung-Bum Pan, and Keun-Chang Kwak. "Multilinear EigenECGs and FisherECGs for Individual Identification from Information Obtained by an Electrocardiogram Sensor." Symmetry 10, no. 10 (2018): 487. http://dx.doi.org/10.3390/sym10100487.

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In this study, we present a third-order tensor-based multilinear eigenECG (MEECG) and multilinear Fisher ECG (MFECG) for individual identification based on the information obtained by an electrocardiogram (ECG) sensor. MEECG and MFECG are based on multilinear principal component analysis (MPCA) and multilinear linear discriminant analysis (MLDA) in the field of multilinear subspace learning (MSL), respectively. MSL directly extracts features without the vectorization of input data, while MSL extracts features without vectorizing the input data while maintaining most of the correlations shown i

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11

Kong, Steffi Shi Qing, Alejandra Martinez, and Maddy Behravan. "Investigation of Normal Cell and Cancer Cell Attachment and the Effects of Ganoderma Lucidum Using an Electric Impedance Sensing Technique." MRS Advances 5, no. 45 (2020): 2341–48. http://dx.doi.org/10.1557/adv.2020.313.

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AbstractThis research introduces an application of an electric impedance sensing technique to investigate cell attachment of normal epithelial cells (HaCAT) and cancerous cells (A431) before and after addition of Ganoderma Lucidum (reishi). In this study, an impedance sensing system is used to measure and characterize real-time changes in electric impedance (resistance and capacitance) with respect to an alternating current (AC) applied to HaCAT and A431 cell colonies. The impedance data is related to the properties of cell spreading, attachment, and delamination. The effect of reishi at dosag

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12

Pichel, Juan C., and Francisco F. Rivera. "Sparse matrix–vector multiplication on the Single-Chip Cloud Computer many-core processor." December 1, 2013. https://doi.org/10.1016/j.jpdc.2013.07.017.

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It is well-known that reordering techniques applied to sparse matrices are common strategies to improve the performance of sparse matrix operations, and particularly, the sparse matrix vector multiplication (SpMV) on CPUs. In this paper, we have evaluated some of the most successful reordering techniques on two different GPUs. In addition, in our study a number of sparse matrix storage formats were con- sidered. Executions for both single and double precision arithmetics were also performed. We have found that SpMV is very sensitive to the application of reordering techniques on GPUs. In parti

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13

Burch, Michael, Kiet Bennema ten Brinke, Adrien Castella, Ghassen Karray Sebastiaan Peters, Vasil Shteriyanov, and Rinse Vlasvinkel. "Dynamic graph exploration by interactively linked node-link diagrams and matrix visualizations." Visual Computing for Industry, Biomedicine, and Art 4, no. 1 (2021). http://dx.doi.org/10.1186/s42492-021-00088-8.

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AbstractThe visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property. For sparse and small graphs, the most efficient approach to such visualization is node-link diagrams, whereas for dense graphs with attached data, adjacency matrices might be the better choice. Because graphs can contain both properties, being globally sparse and locally dense, a combination of several visual metaphors as well as static and dynamic visualizations is beneficial. In this paper, a visually and algorithmically

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14

Vandendijck, Yannick, Oswaldo Gressani, Christel Faes, Carlo G. Camarda, and Niel Hens. "Cohort-based smoothing methods for age-specific contact rates." Biostatistics, March 20, 2023. http://dx.doi.org/10.1093/biostatistics/kxad005.

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Summary The use of social contact rates is widespread in infectious disease modeling since it has been shown that they are key driving forces of important epidemiological parameters. Quantification of contact patterns is crucial to parameterize dynamic transmission models and to provide insights on the (basic) reproduction number. Information on social interactions can be obtained from population-based contact surveys, such as the European Commission project POLYMOD. Estimation of age-specific contact rates from these studies is often done using a piecewise constant approach or bivariate smoot

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15

Šimeček, I. "Performance Aspects of Sparse Matrix-Vector Multiplication." Acta Polytechnica 46, no. 3 (2006). http://dx.doi.org/10.14311/826.

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Sparse matrix-vector multiplication (shortly SpM×V) is an important building block in algorithms solving sparse systems of linear equations, e.g., FEM. Due to matrix sparsity, the memory access patterns are irregular and utilization of the cache can suffer from low spatial or temporal locality. Approaches to improve the performance of SpM×V are based on matrix reordering and register blocking [1, 2], sometimes combined with software-pipelining [3]. Due to its overhead, register blocking achieves good speedups only for a large number of executions of SpM×V with the same matrix A.We have investi

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16

Zeng, Xiaoyun, Yushan Wang, and Guangzhou Li. "Intelligent Analysis of Energy-Saving and Environment-Friendly Concrete Materials Based on Finite Element Simulation and Supercomputer." International Journal of Science and Engineering Applications, January 14, 2023, 79–81. http://dx.doi.org/10.7753/ijsea1201.1025.

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In this paper, based on the finite element simulation method of supercomputer, a three-dimensional intelligent modeling analysis of energy-saving and environmentally friendly concrete materials is carried out. First, the basic principles of the finite element iterative integral equation are introduced. The various characteristics of concrete materials and the interaction between steel and concrete, through calculation, people have a more comprehensive and in-depth grasp of the properties of reinforced concrete structures. In the process of nonlinear finite element analysis of reinforced concre

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17

Ko, Jihoon, Taehyung Kwon, Jinhong Jung, and Kijung Shin. "Effective and lightweight lossy compression of tensors: techniques and applications." Knowledge and Information Systems, June 1, 2025. https://doi.org/10.1007/s10115-025-02471-w.

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Abstract Many real-world data from various domains can be represented as tensors, and a significant portion of them is large scale. Thus, tensor compression is crucial for their storage and transmission. Recently, deep learning-based methods have emerged to enhance compression performance. However, they require considerable compression time to fulfill their performance. In this work, to achieve both speed and performance, we develop ELiCiT, an effective and lightweight lossy tensor compression method. When designing ELiCiT, we avoid deep auto-regressive neural networks and index reordering, wh

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18

Onoda Pessanha, Jose Eduardo, Carlos Enrique Portugal, and Osvaldo R. Saavedra. "An Efficient Reordering Technique for Incomplete Factorization GMRES Preconditioning: Application in Load Flow Problems." International Journal of Emerging Electric Power Systems 12, no. 2 (2011). http://dx.doi.org/10.2202/1553-779x.2343.

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This paper proposes and tests strategies to accomplish high quality incomplete triangular factors (ILU) preconditioners for iteratively solve the load flow sublinear problem. The process begins constructing the preconditioner over the reordered Jacobian matrix calculated in the first Newton iteration. If the iterative process does not start due to high inaccuracies and/or numerical stability problems emerged during the preconditioner construction, the solution process is restarted and the Jacobian matrix calculated in the first Newton iteration is preprocessed through scaling, nonsymmetric and

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