Results 11 to 20 of about 164,297 (154)
Co-sparse Non-negative Matrix Factorization
Frontiers in Neuroscience, 2022 Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property.
Fan Wu +3 more
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Sparse Diagonal Matrix Adaptive SpMV Optimization Method for GPU [PDF]
Jisuanji gongchengSparse Matrix-Vector multiplication (SpMV) is the computational core and bottleneck of sparse linear systems, and its computational efficiency affects the overall performance of iterative solvers.
WANG Yuhua, HE Junfei, ZHANG Yuqi, LAN Haiyan, CAO Linlin
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Deterministic Construction of Binary, Bipolar and Ternary Compressed Sensing Matrices [PDF]
, 2010 In this paper we establish the connection between the Orthogonal Optical Codes (OOC) and binary compressed sensing matrices. We also introduce deterministic bipolar $m\times n$ RIP fulfilling $\pm 1$ matrices of order $k$ such that $m\leq\mathcal{O}\big ...
Arash Amini +2 more
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Equivalence of replica and cavity methods for computing spectra of sparse random matrices [PDF]
Physical Review E, 2011 We show by direct calculation that the replica and cavity methods are exactly equivalent for the spectrum of Erdos-Renyi random graph. We introduce a variational formulation based on the cavity method and use it to find approximate solutions for the density of eigenvalues. We also use this variational method for calculating spectra of sparse covariance
openaire +3 more sources
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets [PDF]
, 2014 Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This manuscript develops a class of highly scalable Nearest Neighbor Gaussian Process (NNGP) models to ...
Banerjee, Sudipto +3 more
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The measured equation of invariance: a new concept in field computation [PDF]
, 1992 Computations of electromagnetic fields are based either on differential equations or on integral equations. The differential equation approach using finite difference or finite element methods results in sparse matrices, which is an advantage, but has to
Chen, Z. +3 more
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Embedded Zassenhaus Expansion to Splitting Schemes: Theory and Multiphysics Applications
International Journal of Differential Equations, 2013 We present some operator splitting methods improved by the use of the Zassenhaus product and designed for applications to multiphysics problems. We treat iterative splitting methods that can be improved by means of the Zassenhaus product formula, which ...
Jürgen Geiser
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A new generalized shift-splitting method for nonsymmetric saddle point problems
Advances in Mechanical Engineering, 2022 Recently, Huang and Huang [ Journal of Computational and Applied Mathematics , 328 (2018) 381–399] proposed a modified generalized shift-splitting preconditioned (denoted by MGSSP) method for solving large sparse saddle point problems, and gave the ...
Tao Wei, Li-Tao Zhang
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IEEE Access, 2023 Diversity measures exploited by blind source separation (BSS) methods are usually based on either statistical attributes/geometrical structures or sparse/overcomplete (underdetermined) representations of the signals.
Muhammad Usman Khalid +2 more
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Spectral methods and computational trade-offs in high-dimensional statistical inference [PDF]
, 2016 Spectral methods have become increasingly popular in designing fast algorithms for modern highdimensional datasets. This thesis looks at several problems in which spectral methods play a central role. In some cases, we also show that such procedures have
Wang, Tengyao
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