Results 11 to 20 of about 2,237,659 (305)
Mueller matrix differential decomposition [PDF]
Optics Letters, 2011 We present a Mueller matrix decomposition based on the differential formulation of the Mueller calculus. The differential Mueller matrix is obtained from the macroscopic matrix through an eigenanalysis. It is subsequently resolved into the complete set of 16 differential matrices that correspond to the basic types of optical behavior for depolarizing ...
Ortega-Quijano, Noé +1 more
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Probability Matrix Decomposition Models [PDF]
Psychometrika, 1996 In this paper, we consider a class of models for two-way matrices with binary entries of 0 and 1. First, we consider Boolean matrix decomposition , conceptualize it as a latent response model (LRM) and, by making use of this conceptualization ...
Maris, E., DeBoeck, P., Mechelen, I. van
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Computing the Polar Decomposition and the Matrix Sign Decomposition in Matrix Groups [PDF]
SIAM Journal on Matrix Analysis and Applications, 2004 The polar decomposition of \(A\) is \(A=UH\), where \(A\) has complex elements, \(U\) is unitary, \(H\) is Hermitian positive semi-definite. The authors identify a family of globally convergent rational iterations that preserve group structure. They show how the structure preservation leads to particularly convenient convergence tests in the case of ...
Nicholas J. Higham +3 more
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Optimal CUR matrix decompositions [PDF]
Proceedings of the forty-sixth annual ACM symposium on Theory of computing, 2014 The CUR decomposition of an $m \times n$ matrix $A$ finds an $m \times c$ matrix $C$ with a subset of $c < n$ columns of $A,$ together with an $r \times n$ matrix $R$ with a subset of $r < m$ rows of $A,$ as well as a $c \times r$ low-rank matrix $U$ such that the matrix $C U R$ approximates the matrix $A,$ that is, $ || A - CUR ||_F^2 \le (1+ε) |
Christos Boutsidis, David P. Woodruff
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Matrix Decomposition and Applications
CoRR, 2022 In 1954, Alston S. Householder published Principles of Numerical Analysis, one of the first modern treatments on matrix decomposition that favored a (block) LU decomposition-the factorization of a matrix into the product of lower and upper triangular matrices.
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The General Linear Model and the Generalized Singular Value Decomposition [PDF]
, 2009 The general linear model with correlated error variables can be transformed by means of the generalized singular value decomposition to a very simple model (canonical form) where the least squares solution is obvious.
Knüsel, Leo
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Jordan Matrix Decomposition [PDF]
Formalized Mathematics, 2008 We follow the rules: i, j, m, n, k denote natural numbers, K denotes a field, and a, λ denote elements of K. Let us consider K, λ, n. The Jordan block of λ and n yields a matrix over K and is defined by the conditions (Def. 1). (Def. 1)(i) len (the Jordan block of λ and n) = n, (ii) width (the Jordan block of λ and n) = n, and (iii) for all i, j such ...
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The General Linear Model and the Generalized Singular Value Decomposition; Some Examples [PDF]
, 2009 The general linear model with correlated error variables can be transformed by means of the generalized singular value decomposition to a very simple model (canonical form) where the least squares solution is obvious.
Knüsel, Leo
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IEEE Access, 2022 In this paper, in order to improve the reliability of Multi-User Multiple-Input Multiple-Output Visible Light Communication (MU-MIMO-VLC) system, we propose a precoding scheme that based on Singular Value Decomposition (SVD) and Geometric Mean ...
Wenduo Qiu, Yan Feng, You Zhang
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Eigenvalue decomposition of a symmetric matrix over the symmetrized max-plus algebra
Desimal, 2021 This paper discusses topics in the symmetrized max-plus algebra. In this study, it will be shown the existence of eigenvalue decomposition of a symmetric matrix over symmetrized max-plus algebra. Eigenvalue decomposition is shown by using a function that
Suroto Suroto
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