Results 11 to 20 of about 49,764 (200)
A New Algorithm for Positive Semidefinite Matrix Completion [PDF]
Journal of Applied Mathematics, 2016 Positive semidefinite matrix completion (PSDMC) aims to recover positive semidefinite and low-rank matrices from a subset of entries of a matrix. It is widely applicable in many fields, such as statistic analysis and system control.
Fangfang Xu, Peng Pan
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On (conditional) positive semidefiniteness in a matrix-valued context [PDF]
Studia Mathematica, 2017 In a nutshell, we intend to extend Schoenberg's classical theorem connecting conditionally positive semidefinite functions $F\colon \mathbb{R}^n \to \mathbb{C}$, $n \in \mathbb{N}$, and their positive semidefinite exponentials $\exp(tF)$, $t > 0$, to the
Gesztesy, Fritz, Pang, Michael
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Matrix Pencils with Coefficients that have Positive Semidefinite Hermitian Parts
SIAM Journal on Matrix Analysis and Applications, 2022 We analyze when an arbitrary matrix pencil is equivalent to a dissipative Hamiltonian pencil and show that this heavily restricts the spectral properties. In order to relax the spectral properties, we introduce matrix pencils with coefficients that have positive semidefinite Hermitian parts. We will make a detailed analysis of their spectral properties
C. Mehl, V. Mehrmann, M. Wojtylak
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Positive Semidefiniteness and Positive Definiteness of a Linear Parametric Interval Matrix [PDF]
, 2017 We consider a symmetric matrix, the entries of which depend linearly on some parameters. The domains of the parameters are compact real intervals. We investigate the problem of checking whether for each (or some) setting of the parameters, the matrix is ...
A Neumaier +30 more
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Low-Rank Positive Semidefinite Matrix Recovery From Corrupted Rank-One Measurements
IEEE Transactions on Signal Processing, 2017 12 pages, 7 ...
Li, Yuanxin, Sun, Yue, Chi, Yuejie
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Computing a nearest symmetric positive semidefinite matrix
Linear Algebra and its Applications, 1988 The problem of computing a nearest positive semidefinite matrix (notation used \(X\geq 0)\) to an arbitrary real matrix A is considered. The criterion of approximation is the distance \(\delta (A)=\min_{X=X^ T\geq 0}\| A-X\|\) where the norm is chosen to be either Frobenius or 2-norm. The paper consists of two parts. In the first part the author proves
Nicholas J Higham
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Norm inequalities for functions of matrices [PDF]
HeliyonIn this paper, we prove several spectral norm and unitarily invariant norm inequalities for matrices in which the special cases of our results present some known inequalities. Also, some of our results give interpolating inequalities which are related to
Ahmad Al-Natoor
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Low-rank matrix approximations over canonical subspaces
Journal of Numerical Analysis and Approximation Theory, 2020 In this paper we derive closed form expressions for the nearest rank-\(k\) matrix on canonical subspaces. We start by studying three kinds of subspaces. Let \(X\) and \(Y\) be a pair of given matrices. The first subspace contains all the \(m\times
Achiya Dax
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Positive semidefinite univariate matrix polynomials [PDF]
Mathematische Zeitschrift, 2018 We study sum-of-squares representations of symmetric univariate real matrix polynomials that are positive semidefinite along the real line. We give a new proof of the fact that every positive semidefinite univariate matrix polynomial of size $n\times n$ can be written as a sum of squares $M=Q^TQ$, where $Q$ has size $(n+1)\times n$, which was recently ...
Hanselka, C., Sinn, R.
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Positive Semidefinite Matrix Factorization Based on Truncated Wirtinger Flow [PDF]
2020 28th European Signal Processing Conference (EUSIPCO), 2021 This paper deals with algorithms for positive semidefinite matrix factorization (PSDMF). PSDMF is a recently-proposed extension of nonnegative matrix factorization with applications in combinatorial optimization, among others. In this paper, we focus on improving the local convergence of an alternating block gradient (ABC) method for PSDMF in a noise ...
Lahat, Dana, Févotte, Cédric
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