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Journal articles on the topic 'Sparse library'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Borštnik, Urban, Joost VandeVondele, Valéry Weber, and Jürg Hutter. "Sparse matrix multiplication: The distributed block-compressed sparse row library." Parallel Computing 40, no. 5-6 (2014): 47–58. http://dx.doi.org/10.1016/j.parco.2014.03.012.

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2

Kómár, Péter, and Marko Kalinić. "Denoising DNA Encoded Library Screens with Sparse Learning." ACS Combinatorial Science 22, no. 8 (2020): 410–21. http://dx.doi.org/10.1021/acscombsci.0c00007.

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3

Muraraşu, Alin, Gerrit Buse, Dirk Pﬂüger, Josef Weidendorfer, and Arndt Bode. "Fastsg: A Fast Routines Library for Sparse Grids." Procedia Computer Science 9 (2012): 354–63. http://dx.doi.org/10.1016/j.procs.2012.04.038.

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4

Zhang, Zuoyu, Shouyi Liao, Hao Fang, Hexin Zhang, and Shicheng Wang. "Sparse Hyperspectral Unmixing Using Spectral Library Adaptive Adjustment." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 12 (2019): 4873–87. http://dx.doi.org/10.1109/jstars.2019.2939829.

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Khatiry Goharoodi, Saeideh, Kevin Dekemele, Mia Loccufier, Luc Dupre, and Guillaume Crevecoeur. "Evolutionary-Based Sparse Regression for the Experimental Identification of Duffing Oscillator." Mathematical Problems in Engineering 2020 (April 29, 2020): 1–12. http://dx.doi.org/10.1155/2020/7286575.

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In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation data. Our purpose is to identify the Coulomb friction terms as part of the ordinary differential equation of the system. Correct identification of this nonlinear system using sparse identification is hugely dependent on selecting the correct form of nonlinearity included in the function library. Consequently, in this work, the evolutionary-based sparse identification is replacing the need for user knowledge when const

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6

Karasawa, Yuki, and Hideya Iwasaki. "Parallel Skeletons for Sparse Matrices in SkeTo Skeleton Library." IPSJ Digital Courier 4 (2008): 167–81. http://dx.doi.org/10.2197/ipsjdc.4.167.

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7

Vuduc, Richard, James W. Demmel, and Katherine A. Yelick. "OSKI: A library of automatically tuned sparse matrix kernels." Journal of Physics: Conference Series 16 (January 1, 2005): 521–30. http://dx.doi.org/10.1088/1742-6596/16/1/071.

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8

Pearce, Roman, and Michael Monagan. "A maple library for high performance sparse polynomial arithmetic." ACM Communications in Computer Algebra 41, no. 3 (2007): 110–11. http://dx.doi.org/10.1145/1358190.1358203.

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9

Qi, Lin, Jie Li, Ying Wang, and Xinbo Gao. "Region-Based Multiview Sparse Hyperspectral Unmixing Incorporating Spectral Library." IEEE Geoscience and Remote Sensing Letters 16, no. 7 (2019): 1140–44. http://dx.doi.org/10.1109/lgrs.2019.2891559.

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10

Belgin, Mehmet, Godmar Back, and Calvin J. Ribbens. "A Library for Pattern-based Sparse Matrix Vector Multiply." International Journal of Parallel Programming 39, no. 1 (2010): 62–87. http://dx.doi.org/10.1007/s10766-010-0145-2.

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11

Drees, L., and R. Roscher. "ARCHETYPAL ANALYSIS FOR SPARSE REPRESENTATION-BASED HYPERSPECTRAL SUB-PIXEL QUANTIFICATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1/W1 (May 30, 2017): 133–39. http://dx.doi.org/10.5194/isprs-annals-iv-1-w1-133-2017.

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This paper focuses on the quantification of land cover fractions in an urban area of Berlin, Germany, using simulated hyperspectral EnMAP data with a spatial resolution of 30m&amp;times;30m. For this, sparse representation is applied, where each pixel with unknown surface characteristics is expressed by a weighted linear combination of elementary spectra with known land cover class. The elementary spectra are determined from image reference data using simplex volume maximization, which is a fast heuristic technique for archetypal analysis. In the experiments, the estimation of class fracti

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12

Feng, Ruyi, Lizhe Wang, and Yanfei Zhong. "Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery." Remote Sensing 10, no. 10 (2018): 1546. http://dx.doi.org/10.3390/rs10101546.

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Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the optimal subset of signatures in this spectral library using the sparse regression technique, and has greatly improved the estimation of fractional abundances in ubiquitous mixed pixels. Since the potentially large standard spectral library can be given a priori, the most challenging task is to compute the regression coefficients, i.e., the fractio

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13

Yang, Shenghao, and Raymond W. Yeung. "Batched Sparse Codes." IEEE Transactions on Information Theory 60, no. 9 (2014): 5322–46. http://dx.doi.org/10.1109/tit.2014.2334315.

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14

Casazza, Peter G., Andreas Heinecke, Felix Krahmer, and Gitta Kutyniok. "Optimally Sparse Frames." IEEE Transactions on Information Theory 57, no. 11 (2011): 7279–87. http://dx.doi.org/10.1109/tit.2011.2160521.

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15

Fayyazi, Hossein, Hamid Dehghani, and Mojtaba Hosseini. "Sparse unmixing of hyper-spectral images using a pruned spectral library." Signal and Data Processing 13, no. 3 (2016): 155–69. http://dx.doi.org/10.18869/acadpub.jsdp.13.3.155.

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16

Yarbrough, Daniel K., Randal Eckert, Jian He, et al. "Rapid Probing of Biological Surfaces with a Sparse-Matrix Peptide Library." PLoS ONE 6, no. 8 (2011): e23551. http://dx.doi.org/10.1371/journal.pone.0023551.

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17

Ortega, G., F. Vazquez, I. Garcia, and E. M. Garzon. "FastSpMM: An Efficient Library for Sparse Matrix Matrix Product on GPUs." Computer Journal 57, no. 7 (2013): 968–79. http://dx.doi.org/10.1093/comjnl/bxt038.

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18

Fresno, Javier, Arturo Gonzalez-Escribano, and Diego R. Llanos. "Extending a hierarchical tiling arrays library to support sparse data partitioning." Journal of Supercomputing 64, no. 1 (2012): 59–68. http://dx.doi.org/10.1007/s11227-012-0757-y.

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19

Yin, Dong, Ramtin Pedarsani, Yudong Chen, and Kannan Ramchandran. "Learning Mixtures of Sparse Linear Regressions Using Sparse Graph Codes." IEEE Transactions on Information Theory 65, no. 3 (2019): 1430–51. http://dx.doi.org/10.1109/tit.2018.2864276.

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20

Burkhardt, Paul. "Optimal Algebraic Breadth-First Search for Sparse Graphs." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (2021): 1–19. http://dx.doi.org/10.1145/3446216.

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There has been a rise in the popularity of algebraic methods for graph algorithms given the development of the GraphBLAS library and other sparse matrix methods. An exemplar for these approaches is Breadth-First Search (BFS). The algebraic BFS algorithm is simply a recurrence of matrix-vector multiplications with the n × n adjacency matrix, but the many redundant operations over nonzeros ultimately lead to suboptimal performance. Therefore an optimal algebraic BFS should be of keen interest especially if it is easily integrated with existing matrix methods. Current methods, notably in the Grap

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21

Bošanský, Michal, and Bořek Patzák. "Evaluation of Different Approaches to Solution of the Direct Solution of Large, Sparse Systems of Linear Equations." Advanced Materials Research 1144 (March 2017): 97–101. http://dx.doi.org/10.4028/www.scientific.net/amr.1144.97.

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The aim of this paper is to the evaluate efficiency of differentapproaches to solution of large, sparse, non-symmetric systems of linearequations on high performance machines, that can be found in any finiteelement software. The different approaches based on direct or iterativealgorithms for solution of linear equations are compared. In particular,directs solver using Skyline sparse storage, direct solver from SuperLUlibrary, iterative solver from Iterative Method Library(IML)are compared. SuperLU is a general purpose library for the directsolution of large, sparse, nonsymmetric systems of lin

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22

Cardellini, Valeria, Salvatore Filippone, and Damian W. I. Rouson. "Design Patterns for Sparse-Matrix Computations on Hybrid CPU/GPU Platforms." Scientific Programming 22, no. 1 (2014): 1–19. http://dx.doi.org/10.1155/2014/469753.

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We apply object-oriented software design patterns to develop code for scientific software involving sparse matrices. Design patterns arise when multiple independent developments produce similar designs which converge onto a generic solution. We demonstrate how to use design patterns to implement an interface for sparse matrix computations on NVIDIA GPUs starting from PSBLAS, an existing sparse matrix library, and from existing sets of GPU kernels for sparse matrices. We also compare the throughput of the PSBLAS sparse matrix–vector multiplication on two platforms exploiting the GPU with that o

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23

Gaidamour, Jérémie, Jonathan Hu, Chris Siefert, and Ray Tuminaro. "Design Considerations for a Flexible Multigrid Preconditioning Library." Scientific Programming 20, no. 3 (2012): 223–39. http://dx.doi.org/10.1155/2012/310508.

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MueLu is a library within the Trilinos software project [An overview of Trilinos, Technical Report SAND2003-2927, Sandia National Laboratories, 2003] and provides a framework for parallel multigrid preconditioning methods for large sparse linear systems. While providing efficient implementations of modern multigrid methods based on smoothed aggregation and energy minimization concepts, MueLu is designed to be customized and extended. This article gives an overview of design considerations for the MueLu package: user interfaces, internal design, data management, usage of modern software constru

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24

Wang, Zhao, Jinxin Wei, Jianzhao Li, Peng Li, and Fei Xie. "Evolutionary Multiobjective Optimization with Endmember Priori Strategy for Large-Scale Hyperspectral Sparse Unmixing." Electronics 10, no. 17 (2021): 2079. http://dx.doi.org/10.3390/electronics10172079.

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Mixed pixels inevitably appear in the hyperspectral image due to the low resolution of the sensor and the mixing of ground objects. Sparse unmixing, as an emerging method to solve the problem of mixed pixels, has received extensive attention in recent years due to its robustness and high efficiency. In theory, sparse unmixing is essentially a multiobjective optimization problem. The sparse endmember term and the reconstruction error term can be regarded as two objectives to optimize simultaneously, and a series of nondominated solutions can be obtained as the final solution. However, the large

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25

Inan, Huseyin A., Peter Kairouz, and Ayfer Ozgur. "Sparse Combinatorial Group Testing." IEEE Transactions on Information Theory 66, no. 5 (2020): 2729–42. http://dx.doi.org/10.1109/tit.2019.2951703.

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26

Demidov, Denis. "AMGCL —A C++ library for efficient solution of large sparse linear systems." Software Impacts 6 (November 2020): 100037. http://dx.doi.org/10.1016/j.simpa.2020.100037.

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27

Sakurai, Takao, Takahiro Katagiri, Hisayasu Kuroda, Ken Naono, Mitsuyoshi Igai, and Satoshi Ohshima. "A Sparse Matrix Library with Automatic Selection of Iterative Solvers and Preconditioners." Procedia Computer Science 18 (2013): 1332–41. http://dx.doi.org/10.1016/j.procs.2013.05.300.

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28

Tommasel, Antonela, Daniela Godoy, and Alejandro Zunino. "SMArtOp : A Java library for distributing high-dimensional sparse-matrix arithmetic operations." Science of Computer Programming 150 (December 2017): 26–30. http://dx.doi.org/10.1016/j.scico.2017.06.005.

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29

Qi, Lin, Jie Li, Xinbo Gao, Ying Wang, Chongyue Zhao, and Yu Zheng. "A novel joint dictionary framework for sparse hyperspectral unmixing incorporating spectral library." Neurocomputing 356 (September 2019): 97–106. http://dx.doi.org/10.1016/j.neucom.2019.04.053.

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30

Kong, Fanqiang, Wenjun Guo, Yunsong Li, Qiu Shen, and Xin Liu. "Backtracking-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/842017.

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Sparse unmixing is a promising approach in a semisupervised fashion by assuming that the observed signatures of a hyperspectral image can be expressed in the form of linear combination of only a few spectral signatures (endmembers) in an available spectral library. Simultaneous orthogonal matching pursuit (SOMP) algorithm is a typical simultaneous greedy algorithm for sparse unmixing, which involves finding the optimal subset of signatures for the observed data from a spectral library. But the numbers of endmembers selected by SOMP are still larger than the actual number, and the nonexisting e

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31

Abdullahi Hassan, Ambra, Valeria Cardellini, Pasqua D’Ambra, Daniela di Serafino, and Salvatore Filippone. "Efficient Algebraic Multigrid Preconditioners on Clusters of GPUs." Parallel Processing Letters 29, no. 01 (2019): 1950001. http://dx.doi.org/10.1142/s0129626419500014.

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Many scientific applications require the solution of large and sparse linear systems of equations using Krylov subspace methods; in this case, the choice of an effective preconditioner may be crucial for the convergence of the Krylov solver. Algebraic MultiGrid (AMG) methods are widely used as preconditioners, because of their optimal computational cost and their algorithmic scalability. The wide availability of GPUs, now found in many of the fastest supercomputers, poses the problem of implementing efficiently these methods on high-throughput processors. In this work we focus on the applicati

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32

Zaslaver, Alon, Idan Liani, Oshrat Shtangel, Shira Ginzburg, Lisa Yee, and Paul W. Sternberg. "Hierarchical sparse coding in the sensory system of Caenorhabditis elegans." Proceedings of the National Academy of Sciences 112, no. 4 (2015): 1185–89. http://dx.doi.org/10.1073/pnas.1423656112.

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Animals with compact sensory systems face an encoding problem where a small number of sensory neurons are required to encode information about its surrounding complex environment. Using Caenorhabditis elegans worms as a model, we ask how chemical stimuli are encoded by a small and highly connected sensory system. We first generated a comprehensive library of transgenic worms where each animal expresses a genetically encoded calcium indicator in individual sensory neurons. This library includes the vast majority of the sensory system in C. elegans. Imaging from individual sensory neurons while

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33

Xu, Jun Wu, and Jun Ling Liang. "Research on a Distributed Storage Application with HBase." Advanced Materials Research 631-632 (January 2013): 1265–69. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1265.

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This paper describes research in the use of HBase to develop storage applications.. This paper introduces the structure of HBase library and describes the implementation of algorithms in our library. HBase is a NoSQL database., A Bigtable is a sparse, distributed, persistent multi-dimensional sorted map. The map is indexed by a row key, column key, and a timestamp; each value in the map is an uninterpreted array of bytes.

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34

Yan, Xiao Fei, and Yan Qiu Wang. "Research on Application of Sparse Representation in Feather and down Category Recognition." Advanced Materials Research 1049-1050 (October 2014): 1297–301. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1297.

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In signal analysis, as a new representation, the sparse representation caused widespread concern of scholars at home and abroad, and signal processing and analysis produced a very significant impact. Feather and Down is closely related to people's lives, different kinds of down, the price difference is bigger, and thermal properties are different, and therefore, feather species identification has always been an important issue. This paper studies the sparse representation in image processing, while the types of detection of Down key technologies studied, proposed a new algorithm to detect the

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35

Saad, Yousef, and Kesheng Wu. "Design of an iterative solution module for a parallel sparse matrix library (P_SPARSLIB)." Applied Numerical Mathematics 19, no. 3 (1995): 343–57. http://dx.doi.org/10.1016/0168-9274(95)00090-9.

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36

Weng, Xuhui, Wuhu Lei, and Xiaodong Ren. "Kernel sparse representation for hyperspectral unmixing based on high mutual coherence spectral library." International Journal of Remote Sensing 41, no. 4 (2019): 1286–301. http://dx.doi.org/10.1080/01431161.2019.1666215.

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37

Laura, Jason, Kelvin Rodriguez, Adam C. Paquette, and Evin Dunn. "AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data." SoftwareX 7 (January 2018): 37–40. http://dx.doi.org/10.1016/j.softx.2018.02.001.

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38

Xu, Ning, Xinyao Xiao, Xiurui Geng, Hongjian You, and Yingui Cao. "Spectral-spatial constrained sparse unmixing of hyperspectral imagery using a hybrid spectral library." Remote Sensing Letters 7, no. 7 (2016): 641–50. http://dx.doi.org/10.1080/2150704x.2016.1177240.

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39

Heckel, Reinhard, and Helmut Bolcskei. "Identification of Sparse Linear Operators." IEEE Transactions on Information Theory 59, no. 12 (2013): 7985–8000. http://dx.doi.org/10.1109/tit.2013.2280599.

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40

Meng Wang, Weiyu Xu, Enrique Mallada, and Ao Tang. "Sparse Recovery With Graph Constraints." IEEE Transactions on Information Theory 61, no. 2 (2015): 1028–44. http://dx.doi.org/10.1109/tit.2014.2376955.

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41

Diederichs, Elmar, Anatoli Juditsky, Vladimir Spokoiny, and Christof Schutte. "Sparse Non-Gaussian Component Analysis." IEEE Transactions on Information Theory 56, no. 6 (2010): 3033–47. http://dx.doi.org/10.1109/tit.2010.2046229.

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42

Gandikota, Venkata, Elena Grigorescu, Sidharth Jaggi, and Samson Zhou. "Nearly Optimal Sparse Group Testing." IEEE Transactions on Information Theory 65, no. 5 (2019): 2760–73. http://dx.doi.org/10.1109/tit.2019.2891651.

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43

Nakos, Vasileios. "Nearly Optimal Sparse Polynomial Multiplication." IEEE Transactions on Information Theory 66, no. 11 (2020): 7231–36. http://dx.doi.org/10.1109/tit.2020.2989385.

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44

Xie, Qianqian, Prayag Tiwari, Deepak Gupta, Jimin Huang, and Min Peng. "Neural variational sparse topic model for sparse explainable text representation." Information Processing & Management 58, no. 5 (2021): 102614. http://dx.doi.org/10.1016/j.ipm.2021.102614.

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45

Cai, T. Tony, and Anru Zhang. "Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices." IEEE Transactions on Information Theory 60, no. 1 (2014): 122–32. http://dx.doi.org/10.1109/tit.2013.2288639.

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46

Vishwas, B. C., Abhishek Gadia, and Mainak Chaudhuri. "Implementing a Parallel Matrix Factorization Library on the Cell Broadband Engine." Scientific Programming 17, no. 1-2 (2009): 3–29. http://dx.doi.org/10.1155/2009/710321.

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Matrix factorization (or often called decomposition) is a frequently used kernel in a large number of applications ranging from linear solvers to data clustering and machine learning. The central contribution of this paper is a thorough performance study of four popular matrix factorization techniques, namely, LU, Cholesky, QR and SVD on the STI Cell broadband engine. The paper explores algorithmic as well as implementation challenges related to the Cell chip-multiprocessor and explains how we achieve near-linear speedup on most of the factorization techniques for a range of matrix sizes. For

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47

Asadi, Mohammadali, Alexander Brandt, Robert H. C. Moir, and Marc Moreno Maza. "Algorithms and Data Structures for Sparse Polynomial Arithmetic." Mathematics 7, no. 5 (2019): 441. http://dx.doi.org/10.3390/math7050441.

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We provide a comprehensive presentation of algorithms, data structures, and implementation techniques for high-performance sparse multivariate polynomial arithmetic over the integers and rational numbers as implemented in the freely available Basic Polynomial Algebra Subprograms (BPAS) library. We report on an algorithm for sparse pseudo-division, based on the algorithms for division with remainder, multiplication, and addition, which are also examined herein. The pseudo-division and division with remainder operations are extended to multi-divisor pseudo-division and normal form algorithms, re

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48

Fitriyani, Fitriyani. "Sliced Coordinate List Implementation Analysis on Sparse Matrix-Vector Multiplication Using Compute Unified Device Architecture." International Journal on Information and Communication Technology (IJoICT) 2, no. 1 (2016): 13. http://dx.doi.org/10.21108/ijoict.2016.21.71.

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<p>Matrices are one of the most used data representation form from real-world problems. Lot of matrix was formed very big but sparse, hence information inside the matrix is relatively small compared to its size. This caused into heavy computational resources needed to process those matrices within short time. One of the solutions to do an efficient process to the sparse matrix is to form it into a specialized form of sparse matrix, such as Sliced Coordinate List (SCOO). SCOO format for sparse matrix has been developed and combined within an implementation using Compute Unified Device Arc

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49

Yang, Honghong, and Shiru Qu. "Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5894639.

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Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is model

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50

HOGAN, JAMES M., and JOACHIM DIEDERICH. "RECRUITMENT LEARNING OF BOOLEAN FUNCTIONS IN SPARSE RANDOM NETWORKS." International Journal of Neural Systems 11, no. 06 (2001): 537–59. http://dx.doi.org/10.1142/s0129065701000953.

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This work presents a new class of neural network models constrained by biological levels of sparsity and weight-precision, and employing only local weight updates. Concept learning is accomplished through the rapid recruitment of existing network knowledge – complex knowledge being realised as a combination of existing basis concepts. Prior network knowledge is here obtained through the random generation of feedforward networks, with the resulting concept library tailored through distributional bias to suit a particular target class. Learning is exclusively local – through supervised Hebbian a

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