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Relevant bibliographies by topics / Matrix Reordering Techniques

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Academic literature on the topic 'Matrix Reordering Techniques'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Matrix Reordering Techniques"

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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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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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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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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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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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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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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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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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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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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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Book chapters on the topic "Matrix Reordering Techniques"

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Freire, Manuel, Raul Marichal, Sanderson L. Gonzaga de Oliveira, Ernesto Dufrechou, and Pablo Ezzatti. "Enhancing the Sparse Matrix Storage Using Reordering Techniques." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52186-7_5.

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Conference papers on the topic "Matrix Reordering Techniques"

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Zhou, Hang, Dongen Yang, Yangfei Lin, Yong Dai, and Quan Chen. "A Parallel Acceleration Technique based on Bordered Block Diagonal Matrix Reordering for Exponential Integrator Method." In 2024 2nd International Symposium of Electronics Design Automation (ISEDA). IEEE, 2024. http://dx.doi.org/10.1109/iseda62518.2024.10617631.

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Wu, Zhenzhi, Dake Liu, and Yanjun Zhang. "Matrix reordering techniques for memory conflict reduction for pipelined QC-LDPC decoder." In 2014 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2014. http://dx.doi.org/10.1109/iccchina.2014.7008301.

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Singh, Uminder, and C. Y. Roger Chen. "A transistor reordering technique for gate matrix layout." In Conference proceedings. ACM Press, 1990. http://dx.doi.org/10.1145/123186.123340.

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Andersen, T. M., J. Torben, K. O. Lye, A. F. Rasmussen, and K. A. Lie. "A Comparison of DILU And ILU(0) as GPU-Accelerated Preconditioners." In SPE Reservoir Simulation Conference. SPE, 2025. https://doi.org/10.2118/223873-ms.

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Abstract We develop auto-tuned GPU implementations of ILU(0) and DILU preconditioners using mixed precision and compare their performance against various GPU-based ILU(0) implementations, integrated into the open-source OPM Flow reservoir simulator. The preconditioners are parallelized conservatively to ensure that the results match those from serial computation up to instruction set equivalence. Our implementation incorporates techniques such as graph coloring, row reordering, matrix splitting, mixed-precision schemes, and auto-tuning to improve runtime efficiency. We evaluate the preconditio

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Namkoong, Kak, Hyoung G. Choi, and J. Y. Yoo. "Computation of the Fluid-Structure Interaction Between an Impinging Jet and a Flexible Plate." In ASME 2002 Joint U.S.-European Fluids Engineering Division Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/fedsm2002-31129.

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For computation of the FSI (Fluid-Structure Interaction) problems, the combined formulation (Hesla, 1991) is adopted which incorporates both the fluid and structure equations of motion into a single coupled variational equation so that it is not necessary to calculate the fluid force on the surface of structure explicitly when solving the equations of motion of the structure. Before tackling complex FSI problems, laminar flow around a freely falling cylinder is considered. The Navier-Stokes equations are solved using a P2P1 Galerkin finite element formulation with ALE (Arbitrary Lagrangian-Eul

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