Results 41 to 50 of about 256,417 (191)
Efficient quantum circuits for Toeplitz and Hankel matrices
, 2016 Toeplitz and Hankel matrices have been a subject of intense interest in a wide range of science and engineering related applications. In this paper, we show that quantum circuits can efficiently implement sparse or Fourier-sparse Toeplitz and Hankel ...
Mahasinghe, A., Wang, J. B.
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Sparse random matrices have simple spectrum [PDF]
Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2020 Let $M_n$ be a class of symmetric sparse random matrices, with independent entries $M_{ij} = _{ij} _{ij}$ for $i \leq j$. $ _{ij}$ are i.i.d. Bernoulli random variables taking the value $1$ with probability $p \geq n^{-1+ }$ for any constant $ > 0$ and $ _{ij}$ are i.i.d. centered, subgaussian random variables.
Luh, Kyle, Vu, Van
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CoD-SELL: A Non-Zero Location Dictionary Compression Sparse Matrix Format for SpMV on GPU
IEEE AccessSparse matrix-vector multiplication (SpMV) is a fundamental computational kernel extensively utilized in scientific computing. To accelerate SpMV, various sparse matrix formats have been proposed.
Shun Murakami +4 more
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Classification of Polarimetric SAR Images Based on the Riemannian Manifold
Leida xuebao, 2017 Classification is one of the core components in the interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. A new PolSAR image classification approach employs the structural properties of the Riemannian manifold formed by PolSAR ...
Yang Wen +3 more
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Logarithmic barriers for sparse matrix cones
, 2012 Algorithms are presented for evaluating gradients and Hessians of logarithmic barrier functions for two types of convex cones: the cone of positive semidefinite matrices with a given sparsity pattern, and its dual cone, the cone of sparse matrices with ...
Andersen, Martin S. +2 more
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Direct multiplicative methods for sparse matrices. Newton methods [PDF]
Компьютерные исследования и моделирование, 2017 We consider a numerically stable direct multiplicative algorithm of solving linear equations systems, which takes into account the sparseness of matrices presented in a packed form. The advantage of the algorithm is the ability to minimize the filling of
Anastasiya Borisovna Sviridenko
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Fast Matrix Multiplication with Big Sparse Data
Cybernetics and Information Technologies, 2017 Big Data becameabuzz word nowadays due to the evolution of huge volumes of data beyond peta bytes. This article focuses on matrix multiplication with big sparse data.
Somasekhar G., Karthikeyan K.
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The Rank Distribution of Sparse Random Linear Network Coding
IEEE Access, 2019 Sparse random linear network coding (SRLNC) is a promising solution for reducing the complexity of random linear network coding (RLNC). RLNC can be modeled as a linear operator channel (LOC).
Wenlin Chen, Fang Lu, Yan Dong
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Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences [PDF]
, 2014 This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas.
Harandi, Mehrtash +3 more
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Sparse Sums of Positive Semidefinite Matrices [PDF]
ACM Transactions on Algorithms, 2015 Many fast graph algorithms begin by preprocessing the graph to improve its sparsity. A common form of this is spectral sparsification, which involves removing and reweighting the edges of the graph while approximately preserving its spectral properties. This task has a more general linear algebraic formulation in terms of approximating sums of rank-one
de Carli Silva, Marcel K. +2 more
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