Results 101 to 110 of about 2,874 (210)
Some Inequalities for Sum and Product of Positive Semidefinite Matrices [PDF]
, 1999 The purpose of this paper is to present some inequalities on majorization, unitarily invariant norm, trace, and eigenvalue for sum and product of positive semidefinite (Hermitian) matrices.
Bo-Yan Xi +5 more
core +1 more source
Detecting When One Probe Vector is Enough for Preconditioned Log‐Determinant Approximation
Numerical Linear Algebra with Applications, Volume 33, Issue 4, August 2026.ABSTRACT We present randomized algorithms for estimating the log‐determinant of regularized symmetric positive semi‐definite matrices. The algorithms access the matrix only through matrix vector products, and are based on the introduction of a preconditioner and stochastic trace estimator.
Alice Cortinovis, Daniele Toni
wiley +1 more source
More on extremal positive semidefinite doubly stochastic matrices [PDF]
, 1992 Let Kn be the convex set of n×n positive semidefinite doubly stochastic matrices. We show that for matrices with a special type of graph extremality can be determined by graph and rank. We also give a complete classification of the extreme matrices in K5,
Berman, Abraham, Shaked-Monderer, Naomi
core +1 more source
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
doaj +1 more source
Measured‐State Conditioned Recursive Feasibility for Stochastic Model Predictive Control
International Journal of Robust and Nonlinear Control, Volume 36, Issue 11, Page 5964-5981, 25 July 2026.ABSTRACT In this paper, we address the problem of designing stochastic model predictive control (SMPC) schemes for linear systems affected by unbounded disturbances. The contribution of the paper is rooted in a measured‐state initialization strategy. First, due to the nonzero probability of violating chance‐constraints in the case of unbounded noise ...
Mirko Fiacchini +2 more
wiley +1 more source
A determinantal inequality for positive semidefinite matrices [PDF]
, 2014 Let A, B, C be n × n positive semidefinite matrices. It is known that det(A + B + C) + det C ≥ det(A + C) + det(B + C), which includes det(A + B) ≥ det A + det B as a special case.
Lin, Minghua
core +1 more source
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.
doaj +1 more source
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
doaj +2 more sources
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
doaj +1 more source
Optimal solution of the nearest correlation matrix problem by minimization of the maximum norm [PDF]
The nearest correlation matrix problem is to find a valid (positive semidefinite) correlation matrix, R(m,m), that is nearest to a given invalid (negative semidefinite) or pseudo-correlation matrix, Q(m,m); m larger than 2.
Mishra, SK
core +1 more source