Results 21 to 30 of about 43,727 (260)
The rank of sparse random matrices [PDF]
Random Structures & Algorithms, 2020 AbstractWe determine the asymptotic normalized rank of a random matrix over an arbitrary field with prescribed numbers of nonzero entries in each row and column. As an application we obtain a formula for the rate of low‐density parity check codes. This formula vindicates a conjecture of Lelarge (2013).
Amin Coja-Oghlan +4 more
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Exhaustive Search for Various Types of MDS Matrices
IACR Transactions on Symmetric Cryptology, 2019 MDS matrices are used in the design of diffusion layers in many block ciphers and hash functions due to their optimal branch number. But MDS matrices, in general, have costly implementations. So in search for efficiently implementable MDS matrices, there
Abhishek Kesarwani +2 more
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Sparse matrices in data analysis [PDF]
Computational Statistics, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nickolay T. Trendafilov +2 more
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New Orthogonal Transforms for Signal and Image Processing
Applied Sciences, 2021 In the paper, orthogonal transforms based on proposed symmetric, orthogonal matrices are created. These transforms can be considered as generalized Walsh–Hadamard Transforms.
Andrzej Dziech
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Journal of Telecommunications and Information Technology, 2019 This paper considers several algorithms for parallelizing the procedure of forward and back substitution for high-order symmetric sparse matrices on multi-core computers with shared memory.
Sergiy Fialko
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Sparse Matrix Based Low-Complexity, Recursive, and Radix-2 Algorithms for Discrete Sine Transforms
IEEE Access, 2021 This paper presents factorizations of each discrete sine transform (DST) matrix of types I, II, III, and IV into a product of sparse, diagonal, bidiagonal, and scaled orthogonal matrices.
Sirani M. Perera, Levi E. Lingsch
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Insights from classifying visual concepts with multiple kernel learning. [PDF]
PLoS ONE, 2012 Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL)
Alexander Binder +7 more
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Direct multiplicative methods for sparse matrices. Linear programming [PDF]
Компьютерные исследования и моделирование, 2017 Multiplicative methods for sparse matrices are best suited to reduce the complexity of operations solving systems of linear equations performed on each iteration of the simplex method.
Anastasiya Borisovna Sviridenko
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On sparse random combinatorial matrices
Discrete Mathematics, 2022 Let $Q_{n,d}$ denote the random combinatorial matrix whose rows are independent of one another and such that each row is sampled uniformly at random from the subset of vectors in $\{0,1\}^n$ having precisely $d$ entries equal to $1$. We present a short proof of the fact that $\Pr[\det(Q_{n,d})=0] = O\left(\frac{n^{1/2}\log^{3/2} n}{d}\right)=o(1 ...
Elad Aigner-Horev, Yury Person
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Lower bounds for sparse matrix vector multiplication on hypercubic networks [PDF]
Discrete Mathematics & Theoretical Computer Science, 1998 In this paper we consider the problem of computing on a local memory machine the product y = Ax,where A is a random n×n sparse matrix with Θ(n) nonzero elements.
Giovanni Manzini
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