Results 31 to 40 of about 1,052,390 (266)
Practical method to solve large least squares problems using Cholesky decomposition
Geodesy and Cartography, 2015 In Geomatics, the method of least squares is commonly used to solve the systems of observation equations for a given number of unknowns. This method is basically implemented in case of having number observations larger than the number of unknowns ...
Ghadi Younis
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Symmetric indefinite triangular factorization revealing the rank profile matrix [PDF]
, 2018 We present a novel recursive algorithm for reducing a symmetric matrix to a triangular factorization which reveals the rank profile matrix. That is, the algorithm computes a factorization $\mathbf{P}^T\mathbf{A}\mathbf{P} = \mathbf{L}\mathbf{D}\mathbf{L}^
Dumas, Jean-Guillaume, Pernet, Clement
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Pivot-Free Block Matrix Inversion [PDF]
2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2006 We present a pivot-free deterministic algorithm for the inversion of block matrices. The method is based on the Moore-Penrose inverse and is applicable over certain general classes of rings. This improves on previous methods that required at least one invertible on-diagonal block, and that otherwise required row- or column-based pivoting, disrupting ...
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Lossy Compression using Adaptive Polynomial Image Encoding
Advances in Electrical and Computer Engineering, 2021 In this paper, an efficient lossy compression approach using adaptive-block polynomial curve-fitting encoding is proposed. The main idea of polynomial curve fitting is to reduce the number of data elements in an image block to a few coefficients.
OTHMAN, S. +3 more
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Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization
Computers, 2022 To meet the high bit rate requirements in many multimedia applications, a lossy image compression algorithm based on Walsh–Hadamard kernel-based feature extraction, discrete cosine transform (DCT), and bi-level quantization is proposed in this paper. The
Dibyalekha Nayak +3 more
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Isospectral flows on a class of finite-dimensional Jacobi matrices
, 2012 We present a new matrix-valued isospectral ordinary differential equation that asymptotically block-diagonalizes $n\times n$ zero-diagonal Jacobi matrices employed as its initial condition. This o.d.e.\ features a right-hand side with a nested commutator
Chatterjee, Debasish +3 more
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A block Hankel generalized confluent Vandermonde matrix [PDF]
, 2012 Vandermonde matrices are well known. They have a number of interesting properties and play a role in (Lagrange) interpolation problems, partial fraction expansions, and finding solutions to linear ordinary differential equations, to mention just a few ...
Klein, Andre, Spreij, Peter
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USE OF THE TREND-FACTOR MODEL TO IMPROVE THE ACCURACY FORECASTS
Статистика и экономика, 2016 In this paper we propose a method toimprove the accuracy of the trend-factormodel on the assumption that the increasein endogenous variable-screens dependnot only on time but also deviations fromtheir trend of exogenous variables, provedby the ...
Irina V. Orlova, Viktor B. Turundaevsky
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Deep Subspace Clustering with Block Diagonal Constraint
Applied Sciences, 2020 The deep subspace clustering method, which adopts deep neural networks to learn a representation matrix for subspace clustering, has shown good performance.
Jing Liu, Yanfeng Sun, Yongli Hu
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On the Marginal Distribution of the Diagonal Blocks in a Blocked Wishart Random Matrix [PDF]
International Journal of Analysis, 2016 Let A be a (m1+m2)×(m1+m2) blocked Wishart random matrix with diagonal blocks of orders m1×m1 and m2×m2. The goal of the paper is to find the exact marginal distribution of the two diagonal blocks of A. We find an expression for this marginal density involving the matrix-variate generalized hypergeometric function.
Víctor Ayala +3 more
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