Results 91 to 100 of about 2,871 (211)
Advanced Quantum Technologies, Volume 9, Issue 6, June 2026.Network monitoring is fundamental to effective traffic engineering (TE) in quantum networks and although nondestructive techniques such as weak measurement, quantum non‐demolition (QND) measurement, and protective measurement have been proposed, their roles in supporting TE have not been systematically examined. This paper proposes a unified analytical
Joachim Notcker +4 more
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Restricted Riemannian geometry for positive semidefinite matrices
, 2023 We introduce the manifold of {\it restricted} $n\times n$ positive semidefinite matrices of fixed rank $p$, denoted $S(n,p)^{*}$. The manifold itself is an open and dense submanifold of $S(n,p)$, the manifold of $n\times n$ positive semidefinite matrices
Sun, Qiang +2 more
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From ƒ-Divergence to Quantum Quasi-Entropies and Their Use
Entropy, 2010 Csiszár’s ƒ-divergence of two probability distributions was extended to the quantum case by the author in 1985. In the quantum setting, positive semidefinite matrices are in the place of probability distributions and the quantum generalization is called ...
Dénes Petz
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Distributed Optimization of Finite Condition Number for Laplacian Matrix in Multi‐Agent Systems
International Journal of Robust and Nonlinear Control, Volume 36, Issue 9, Page 5030-5043, June 2026.ABSTRACT This paper addresses the distributed optimization of the finite condition number of the Laplacian matrix in multi‐agent systems. The finite condition number, defined as the ratio of the largest to the second smallest eigenvalue of the Laplacian matrix, plays an important role in determining the convergence rate and performance of consensus ...
Yicheng Xu, Faryar Jabbari
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Extremal positive semidefinite doubly stochastic matrices
, 1991 Let Kn denote the closed convex set of all n-by-n positive semidefinite doubly stochastic matrices. The extreme points of Kn have not been determined. In this paper, we find some extreme points.
Pierce, Steve, Grone, Bob
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Covariance Estimation for Wide Data
WIREs Computational Statistics, Volume 18, Issue 2, June 2026.Covariance matrix estimation is fundamental to multivariate analysis, with applications spanning finance, genomics, climate science, and signal processing. This review synthesizes recent advances in high‐dimensional covariance estimation‐thresholding, linear and nonlinear shrinkage, graphical models, and random matrix theory‐under a unifying framework ...
Eran Raviv
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Special MatricesGiven matrices AA and BB of the same order, AA is called a section of BB if R(A)∩R(B−A)={0}{\mathscr{R}}\left(A)\cap {\mathscr{R}}\left(B-A)=\left\{0\right\} and R(AT)∩R((B−A)T)={0}{\mathscr{R}}\left({A}^{T})\cap {\mathscr{R}}\left({\left(B-A)}^{T ...
Eagambaram N.
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Journal of Numerical Analysis and Approximation Theory, 2010 In this paper, we obtain new results concerning the generalizations of additive and multiplicative majorizations by means of exponential convexity. We prove positive semi-definiteness of matrices generated by differences deduced from majorization type ...
Naveed Latif, Josip Pečarić
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Mokslas: Lietuvos Ateitis, 2015 Semidefinite Programming (SDP) is a fairly recent way of solving optimization problems which are becoming more and more important in our fast moving world. It is a minimization of linear function over the intersection of the cone of positive semidefinite
Rasa Giniūnaitė
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Oppenheim–Schur inequalities for causal products
Bulletin of the London Mathematical Society, Volume 58, Issue 6, June 2026.Abstract We establish a class of Oppenheim–Schur‐type inequalities for the convolutional Jury product of positive semidefinite matrices. These results extend the classical Schur and Oppenheim inequalities associated with the Hadamard product to a causal convolutional setting.
Dominique Guillot +2 more
wiley +1 more source