Results 11 to 20 of about 16,194,429 (375)
Open Physics, 2022 The main aim of this article is to study a new generalizations of the Gauss hypergeometric matrix and confluent hypergeometric matrix functions by using two-parameter Mittag–Leffler matrix function.
Jain Shilpi +4 more
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Structure and Function of Human Matrix Metalloproteinases
Cells, 2020 The extracellular matrix (ECM) is a macromolecules network, in which the most abundant molecule is collagen. This protein in triple helical conformation is highly resistant to proteinases degradation, the only enzymes capable of degrading the collagen ...
Helena Laronha, Jorge Caldeira
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On the hypergeometric matrix function
Journal of Computational and Applied Mathematics, 1998 AbstractThis paper deals with the study of the hypergeometric function with matrix arguments F(A,B;C;z). Conditions for matrices A, B, C so that the series representation of the hypergeometric function be convergent for ¦z¦ = 1 and satisfies a matrix differential equation are given.
Juan Carlos Cortés, Lucas Jódar
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Regular approximate factorization of a class of matrix-function with an unstable set of partial indices. [PDF]
Proc Math Phys Eng Sci, 2018 From the classic work of Gohberg & Krein (1958 Uspekhi Mat. Nauk. XIII, 3–72. (Russian).), it is well known that the set of partial indices of a non-singular matrix function may change depending on the properties of the original matrix.
Mishuris G, Rogosin S.
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On the matrix function $_pR_q(A, B; z)$ and its fractional calculus properties [PDF]
Communications in Mathematics, 2022 The main objective of the present paper is to introduce and study the function $_pR_q(A, B; z)$ with matrix parameters and investigate the convergence of this matrix function.
Ravi Dwivedi, Reshma Sanjhira
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Fast computation of matrix function-based centrality measures for layer-coupled multiplex networks. [PDF]
Physical Review E, 2021 Centrality measures identify and rank the most influential entities of complex networks. In this paper, we generalize matrix function-based centrality measures, which have been studied extensively for single-layer and temporal networks in recent years to
Kai Bergermann, M. Stoll
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Error bounds for Lanczos-based matrix function approximation [PDF]
SIAM Journal on Matrix Analysis and Applications, 2021 We analyze the Lanczos method for matrix function approximation (Lanczos-FA), an iterative algorithm for computing $f(\mathbf{A}) \mathbf{b}$ when $\mathbf{A}$ is a Hermitian matrix and $\mathbf{b}$ is a given mathbftor.
Tyler Chen +3 more
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Stability of the Lanczos Method for Matrix Function Approximation [PDF]
ACM-SIAM Symposium on Discrete Algorithms, 2017 The ubiquitous Lanczos method can approximate $f(A)x$ for any symmetric $n \times n$ matrix $A$, vector $x$, and function $f$. In exact arithmetic, the method's error after $k$ iterations is bounded by the error of the best degree-$k$ polynomial ...
Cameron Musco +2 more
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U-duality from Matrix Membrane Partition Function [PDF]
, 2001 We analyse supermembrane instantons (fully wrapped supermembranes) by computing the partition function of the three-dimensional supersymmetrical U(N) matrix model under periodic boundary conditions.
Affleck +36 more
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Computing matrix functions [PDF]
Acta Numerica, 2010 The need to evaluate a functionf(A)∈ ℂn×nof a matrixA∈ ℂn×narises in a wide and growing number of applications, ranging from the numerical solution of differential equations to measures of the complexity of networks. We give a survey of numerical methods for evaluating matrix functions, along with a brief treatment of the underlying theory and a ...
Higham, Nicholas J., Al-Mohy, Awad H.
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