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Extended Beta and Gamma Matrix Functions via 2-Parameter Mittag-Leffler Matrix Function
Mathematics, 2022 The main aim of this article is to study an extension of the Beta and Gamma matrix functions by using a two-parameter Mittag-Leffler matrix function. In particular, we investigate certain properties of these extended matrix functions such as symmetric ...
Rahul Goyal +3 more
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On a Characterisation of Matrix Functions which are Differences of Two Monotone Matrix Functions [PDF]
Proceedings of the American Mathematical Society, 1972 The class of matrix functions of ’bounded variation’ was introduced by O. Dobsch in a paper published in 1937 [2]. The consideration of this class of functions immediately gives rise to the consideration of those matrix functions of order n on an interval [a, b] that are representable as the difference of two monotone matrix functions on that interval.
Harkrishan Lal Vasudeva
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Generalized Matrix Functions [PDF]
Transactions of the American Mathematical Society, 1965 Marvin Marcus, Henryk Minc
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Axioms, 2023 This paper considers the computation of approximations of matrix functionals of form F(A):=vTf(A)v, where A is a large symmetric positive definite matrix, v is a vector, and f is a Stieltjes function.
Jihan Alahmadi +2 more
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SoftNet: A Package for the Analysis of Complex Networks
Algorithms, 2022 Identifying the most important nodes according to specific centrality indices is an important issue in network analysis. Node metrics based on the computation of functions of the adjacency matrix of a network were defined by Estrada and his collaborators
Caterina Fenu +2 more
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Derivative and higher-order Cauchy integral formula of matrix functions
Open Mathematics, 2021 The derivative of a nn-order matrix function on the complex field is usually defined as a n2{n}^{2}-order matrix, which is not suitable for generalizing Cauchy integral formula of matrix functions to its higher-order derivative form. In this paper, a new
Huang Xiaojie, Liu Zhixiu, Wu Chun
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Marginal Fisher Analysis With Polynomial Matrix Function
IEEE Access, 2022 Marginal fisher analysis (MFA) is a dimensionality reduction method based on a graph embedding framework. In contrast to traditional linear discriminant analysis (LDA), which requires the data to follow a Gaussian distribution, MFA is suitable for non ...
Ruisheng Ran +4 more
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Fractal and Fractional, 2023 The computation of the sign function of a matrix plays a crucial role in various mathematical applications. It provides a matrix-valued mapping that determines the sign of each eigenvalue of a nonsingular matrix.
Lei Shi +4 more
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Efficient Evaluation of Matrix Polynomials beyond the Paterson–Stockmeyer Method
Mathematics, 2021 Recently, two general methods for evaluating matrix polynomials requiring one matrix product less than the Paterson–Stockmeyer method were proposed, where the cost of evaluating a matrix polynomial is given asymptotically by the total number of matrix ...
Jorge Sastre, Javier Ibáñez
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Differentiating matrix functions [PDF]
Operators and Matrices, 2013 Multivariable, real-valued functions induce matrix-valued functions defined on the space of d-tuples of n-times-n pairwise-commuting self-adjoint matrices. We examine the geometry of this space of matrices and conclude that the best notion of differentiation of these matrix-valued functions is differentiation along curves.
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