Results 221 to 230 of about 41,020 (249)

A Measure Of Asymmetry For Positive Semidefinite Matrices

Optimization, 2003
It is known that a continuous map is the gradient of a convex function if and only if it is cyclically monotone. Also, a differentiable map F is the gradient of a function if and only if the matrices F ′(x) are symmetric for all x in the domain. Based on this connection between symmetry and monotonicity, we define a measure of asymmetry for positive ...
Crouzeix, Jean-Pierre, Gutan, G.
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Norm inequalities for positive semidefinite matrices [PDF]

Wuhan University Journal of Natural Sciences, 2012
This paper aims to discuss some inequalities involving unitarily invariant norms and positive semidefinite matrices. By using properties of unitarily invariant norms, we obtain two inequities involving unitarily invariant norms and positive semidefinite matrices, which generalize the result obtained by Bhatia and Kittaneh.
Yanqiu Wu, Limin Zou, Limin Zou
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Positive Semidefinite Matrices

1999
This chapter studies the positive semidefinite matrices, concentrating primarily on the inequalities of this type of matrix. The main goal is to present the fundamental results and show some often-used techniques. Section 7.1 gives the basic properties, Section 7.2 treats the L¨owner partial ordering of positive semidefinite matrices, and Section 7.3 ...
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Positive Semidefinite Matrices

2018
Positive semidefinite (PSD) and positive definite (PD) matrices are closely connected with Euclidean distance matrices. Accordingly, they play a central role in this monograph. This chapter reviews some of the basic results concerning these matrices.
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Semidefinite Programming in the Space of Partial Positive Semidefinite Matrices

SIAM Journal on Optimization, 2003
We build upon the work of Fukuda et al. [SIAM J. Optim., 11 (2001), pp. 647--674] and Nakata et al. [Math. Program., 95 (2003), pp. 303--327], in which the theory of partial positive semidefinite matrices was applied to the semidefinite programming (SDP) problem as a technique for exploiting sparsity in the data.
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Transformation of non positive semidefinite correlation matrices

Communications in Statistics - Theory and Methods, 1993
In multivariate statistics, estimation of the covariance or correlation matrix is of crucial importance. Computational and other arguments often lead to the use of coordinate-dependent estimators, yielding matrices that are symmetric but not positive semidefinite.
Rousseeuw, Peter, Molenberghs, Geert
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Matrices with positive semidefinite real part

Linear and Multilinear Algebra, 2019
Matrices with the property that the real part is positive definite, have been studied for the past five decades or more.
Choudhury, Projesh Nath, Sivakumar, KC
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On Sylvester's criterion for positive-semidefinite matrices

IEEE Transactions on Automatic Control, 1973
The error contained in several engineering texts on systems theory regarding Sylvester's criterion for positive-semidefinite matrices is brought to the fore.
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Systemic therapy for hormone receptor‐positive/human epidermal growth factor receptor 2‐negative early stage and metastatic breast cancer

Ca-A Cancer Journal for Clinicians, 2023
Laura A Huppert, Hope S Rugo
exaly  

Antibiotic resistance in the patient with cancer: Escalating challenges and paths forward

Ca-A Cancer Journal for Clinicians, 2021
Amila K Nanayakkara, Vance G Fowler, Kevin Outterson   +2 more
exaly