An ideal I of a ring R is called right Baer-ideal if there exists an idempotent e 2 R such that r(I) = eR. We know that R is quasi-Baer if every ideal of R is a right Baer-ideal, R is n-generalized right quasi-Baer if for each I E R the ideal In is right Baer-ideal, and R is right principaly quasi-Baer if every principal right ideal of R is a right Baer-ideal. Therefore the concept of Baer idea...

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 9 July 2021Accepted: 21 September 2021Published online: 13 January 2022Keywordsinertial block coordinate method, majorization minimization, Bregman surrogate function, acceleration by extrapolation, orthogonal nonnegative matrix factorizationAMS Subject Headings90C26, 49M...

In multivariate statistical analysis. orthogonally invariant sets of real positive definite pxp matrices occur as acceptance regions for tests of invariant hypotheses concerning the covariance matrix [ of a multivariate normal distribution. Equivalently. orthogonally invariant acceptance regions can be expressed in terms of the eigenvalues I, (S). .... lp(S) of a random Wishart matrix S Wp(n. [...

In this paper, we aim at finding a nearest correlation matrix to a given symmetric matrix, measured by the componentwise weighted Frobenius norm, with a prescribed rank and bound constraints on its correlations. This is in general a non-convex and difficult problem due to the presence of the rank constraint. To deal with this difficulty, we first consider a penalized version of this problem and...

This paper presents majorization results for PH-generators. Based on the majorization results, bounds on the moments and Laplace-Stieltjes transforms of phase-type distributions are found. Exponential distributions and Coxian distributions are identified to be extremal PH-distributions with respect to all the moments and Laplace-Stieltjes transforms for certain subsets of PH-distributions. UN C...

The singular value decomposition (SVD) is not only a classical theory in matrix computation and analysis, but also is a powerful tool in machine learning and modern data analysis. In this tutorial we first study the basic notion of SVD and then show the central role of SVD in matrices. Using majorization theory, we consider variational principles of singular values and eigenvalues. Built on SVD...

We prove several results majorizing the sequences of Kronecker and/or Jordan indices obtainable after small perturbations to a given matrix pencil. The proofs are simple consequences of a theory of majorization for semi-innnite integer sequences, developed in this paper. In particular , new simple bounds are proved on the indices obtainable after appending a single row or column to a matrix pen...

where B is a real matrix with m rows and n columns, x ranges over the set of row vectors with m components, all non-negative and adding up to one, y ranges over the corresponding set of «-component column vectors, and the pay-off, (x2?, y), indicates the inner product of the two vectors xB and y. One device which may simplify a game computation is that of "dominance" or "majorization" [vNM, p. ...

In this paper we study the concept of Latin-majorizati-\on. Geometrically this concept is different from other kinds of majorization in some aspects. Since the set of all $x$s Latin-majorized by a fixed $y$ is not convex, but, consists of union of finitely many convex sets. Next, we hint to linear preservers of Latin-majorization on $ mathbb{R}^{n}$ and ${M_{n,m}}$.