Results 11 to 20 of about 992,671 (173)
Numerical Range of Moore–Penrose Inverse Matrices
Mathematics, 2020 Let A be an n-by-n matrix. The numerical range of A is defined as W ( A ) = { x * A x : x ∈ C n , x * x = 1 } . The Moore–Penrose inverse A + of A is the unique matrix satisfying A A + A = A , A + A A + = A ...
Mao-Ting Chien
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On matrix convexity of the Moore-Penrose inverse [PDF]
International Journal of Mathematics and Mathematical Sciences, 1996 Matrix convexity of the Moore-Penrose inverse was considered in the recent literature. Here we give some converse inequalities as well as further generalizations.
B. Mond, J. E. Pecaric
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On mixed-type reverse-order laws for the Moore-Penrose inverse of a matrix product [PDF]
International Journal of Mathematics and Mathematical Sciences, 2004 Some mixed-type reverse-order laws for the Moore-Penrose inverse of a matrix product are established. Necessary and sufficient conditions for these laws to hold are found by the matrix rank method.
Yongge Tian
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On the Sequential Composition of the Moore-Penrose Matrix Inverse [PDF]
, 2023 <p>A technical note on the sequential compositionality of the Moore-Penrose Matrix Inverse.
Aziz, Benjamin
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International Journal of Robust and Nonlinear Control, EarlyView., 2023 Abstract We propose a hierarchical energy management scheme for aggregating Distributed Energy Resources (DERs) for grid flexibility services. To prevent a direct participation of numerous prosumers in the wholesale electricity market, aggregators, as self‐interest agents in our scheme, incentivize prosumers to provide flexibility. We firstly model the
Xiupeng Chen +3 more
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Convergence of Rump's method for computing the Moore-Penrose inverse [PDF]
, 2016 summary:We extend Rump's verified method (S. Oishi, K. Tanabe, T. Ogita, S. M. Rump (2007)) for computing the inverse of extremely ill-conditioned square matrices to computing the Moore-Penrose inverse of extremely ill-conditioned rectangular matrices ...
Chen, Yunkun, Wei, Yimin, Shi, Xinghua
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Expressions and characterizations for the Moore-Penrose inverse [PDF]
, 2023 Under certain conditions, we prove that the Moore-Penrose inverse of a sum of operators is the sum of the Moore-Penrose inverses. From this, we derive expressions and characterizations for the Moore-Penrose inverse of an operator that are useful for its ...
Patricia Morillas +2 more
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On the mean and variance of the estimated tangency portfolio weights for small samples
Modern Stochastics: Theory and Applications, 2022 In this paper, a sample estimator of the tangency portfolio (TP) weights is considered. The focus is on the situation where the number of observations is smaller than the number of assets in the portfolio and the returns are i.i.d.
Gustav Alfelt, Stepan Mazur
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The dual index and dual core generalized inverse
Open Mathematics, 2023 In this article, we introduce the dual index and dual core generalized inverse (DCGI). By applying rank equation, generalized inverse, and matrix decomposition, we give several characterizations of the dual index when it is equal to 1. We realize that if
Wang Hongxing, Gao Ju
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Idempotent operator and its applications in Schur complements on Hilbert C*-module
Special Matrices, 2023 The present study proves that TT is an idempotent operator if and only if R(I−T∗)⊕R(T)=X{\mathcal{ {\mathcal R} }}\left(I-{T}^{\ast })\oplus {\mathcal{ {\mathcal R} }}\left(T)={\mathcal{X}} and (T∗T)†=(T†)2T{\left({T}^{\ast }T)}^{\dagger }={\left({T ...
Karizaki Mehdi Mohammadzadeh +1 more
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