More inequalities for positive semidefinite matrices
In this paper, we first present a necessary and sufficient condition for a class of block matrices to be positive semidefinite. Second, we demonstrate the significance of a known inequality (as presented in [5]) through a norm inequality. Finally, utilizing the polar decomposition, we provide a functional version of a singular value inequality.
Feng Zhang, Hefang Jing
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A sequential semidefinite programming method and an application in passive reduced-order modeling
We consider the solution of nonlinear programs with nonlinear semidefiniteness constraints. The need for an efficient exploitation of the cone of positive semidefinite matrices makes the solution of such nonlinear semidefinite programs more complicated ...
Freund, Roland W. +2 more
core
Preconditioning by an extended matrix technique for convection-diffusion-reaction equations
In this paper we consider a preconditioning technique for the ill-conditioned systems arising from discretisations of nonsymmetric elliptic boundary value problems.
Aurelian Nicola, Constantin Popa
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TRACE INEQUALITIES OF POSITIVE SEMIDEFINITE MATRICES
In this paper, the trace inequalities involving special products of the positive semidefinite matrices are investigated. The trace inequalities between the Kronecker product and Kronecker sum of two matrices is obtained as in the short note Yang’s inequalities.
ÖZEL, Mustafa +3 more
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Inequalities Involving Hadamard Products of Positive Semidefinite Matrices
This paper is an extension of two inequalities. An inequality established by \textit{G. P. H. Styan} [Linear Algebra Appl. 6, 217-240 (1973; Zbl 0255.15002)] is on the Hadamard product and a correlation matrix. An inequality obtained by \textit{B. Wang} and \textit{F. Zhang} [Linear Multilinear Algebra 43, No.
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On classes of matrices containing M-matrices and hermitian positive semidefinite matrices
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Sparsifying Sums of Positive Semidefinite Matrices
In this paper, we revisit spectral sparsification for sums of arbitrary positive semidefinite (PSD) matrices. Concretely, for any collection of PSD matrices $\mathcal{A} = \{A_1, A_2, \ldots, A_r\} \subset \mathbb{R}^{n \times n}$, given any subset $T \subseteq [r]$, our goal is to find sparse weights $μ\in \mathbb{R}_{\geq 0}^r$ such that $(1 - ε ...
Basu, Arpon +3 more
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On ㏒ majorizations for positive semidefinite matrices [PDF]
C.-S. Lin, Yeol Je Cho
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Constrained shadow tomography for molecular simulation on quantum devices. [PDF]
Avdic I +6 more
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Secrecy Rate Maximization for Movable Antenna-Aided STAR-RIS in Integrated Sensing and Communication Systems. [PDF]
Chen G +6 more
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