Universal similarity factorization equalities over real Clifford algebras

A variety of universal similarity factorization equalities over real Clifford algebrasR _p,q are established. On the basis of these equalities, real, complex and quaternion matrix representations of elements inR _p,q can easily be explicitly be determined.     

三个矩阵乘积厄米特Moore-Penrose逆的等式与不等式

We investigate relationships between the Moore-Penrose inverse(ABA*)and the product [(AB)(1,2,3)]*B(AB)(1,2,3)through some rank and inertia formulas for the difference of(ABA*)-[(AB)(1,2,3)]*B(AB)(1,2,3),where B is Hermitian matrix and(AB)(1,2,3)is a {1,2,3}-inverse of AB.We show that there always exists an(AB)(1,2,3)such that(ABA*)= [(AB)(1,2,3)]*B(AB)(1,2,3)holds.In addition,we also establish necessary and sufficient conditions for the two inequalities(ABA*) [(AB)(1,2,3)]*B(AB)(1,2,3)and(ABA*)[(AB)(1,2,3)]*B(AB)(1,2,3)to hold in the L¨owner partial ordering.Some variations of the equalities and inequalities are also presented.In particular,some equalities and inequalities for the Moore-Penrose inverse of the sum A + B of two Hermitian matrices A and B are established.     

More on extremal ranks of the matrix expressions A − BX ± X*B* with statistical applications

Through a Hermitian‐type (skew‐Hermitian‐type) singular value decomposition for pair of matrices (A, B) introduced by Zha (Linear Algebra Appl. 1996; 240 :199–205), where A is Hermitian (skew‐Hermitian), we show how to find a Hermitian (skew‐Hermitian) matrix X such that the matrix expressions A ? BX ± X^*B^* achieve their maximal and minimal possible ranks, respectively. For the consistent matrix equations BX ± X^*B^* = A, we give general solutions through the two kinds of generalized singular value decompositions. As applications to the general linear model {y, Xβ, σ²V}, we discuss the existence of a symmetric matrix G such that Gy is the weighted least‐squares estimator and the best linear unbiased estimator of Xβ, respectively. Copyright © 2007 John Wiley & Sons, Ltd.     

More on generalized inverses of partitioned matrices with Banachiewicz–Schur forms

Necessary and sufficient conditions are derived for a 2-by-2 partitioned matrix to have {1}-, {1,2}-, {1,3}-, {1,4}-inverses and the Moore–Penrose inverse with Banachiewicz–Schur forms. As applications, the Banachiewicz–Schur forms of {1}-, {1,2}-, {1,3}-, {1,4}-inverses and the Moore–Penrose inverse of a 2-by-2 partitioned Hermitian matrix are also given.     

Bifunctional inhibitors of mevalonate kinase and mevalonate 5-diphosphate decarboxylase

[structure: see text] A bifunctional inhibitor of mevalonate kinase and mevalonate 5-diphosphate decarboxylase was synthesized. Both enzymes are in the cholesterol biosynthetic pathway and play an important role in regulating cholesterol biosynthesis. The molecule may become a useful lead compound for further development for treating cardiovascular disease and cancer. This study provides a novel example of a single inhibitor blocking two sequential steps simultaneously in the cholesterol biosynthetic pathway.     

Invariance properties of a triple matrix product involving generalized inverses

Given complex-valued matrices A, B and C of appropriate dimensions, this paper investigates certain invariance properties of the product AXC with respect to the choice of X, where X is a generalized inverse of B. Different types of generalized inverses are taken into account. The purpose of the paper is three-fold: First, to review known results scattered in the literature, second, to demonstrate the connection between invariance properties and the concept of extremal ranks of matrices, and third, to add new results related to the topic.     

The Maximal and Minimal Ranks of Some Expressions of Generalized Inverses of Matrices

In this paper, we first determine the maximal and minimal ranks of A — BXC with respect to X. Using those results, we then find the maximal and minimal ranks of the expressions AA^– — A^– A — BB^– A — AC^– C and B^– BA — ACC^– with respect to the choice of generalized inverses A^–, B^– and C^–. In particular, we consider the commutativity of A and A^–, A^k and A^–.The research of the author was supported in part by the Natural Sciences and Engineering Research Council of Canada.     

Hydrothermal synthesis and structure of a new helical chain constructed from only molybdenum-oxide building blocks

The compound [NH(3)(CH(2))(2)NH(2)(CH(2))(2)NH(3)](2)[Mo(9)O(30)] was synthesized at 170 degrees C via hydrothermal techniques from the starting materials of Na(2)MoO(4).H(2)O, NH(2)(CH(2))(2)NH(CH(3))(2)NH(2), and H(2)O. The compound crystallizes in the space group C2/c of the monoclinic system with a = 21.308(4) A, b = 9.234(2) A, c = 19.195(4) A, beta = 120.27(3) degrees, V = 3261.9(11)A(3), Z = 4. Two novel and symmetrically related helices coexist in the centrosymmetric solid, in which the two kinds of helices appear in the left-handed and right-handed enantiomorphs, respectively. This is the first example of a helical chain structure consisting of only molybdenum-oxide building blocks. The central axis about each helical chain is a 2-fold screw axis.     

An algebraic study of BLUPs under two linear random-effects models with correlated covariance matrices

Assume that a pair of general Linear Random-effects Models (LRMs) are given with a correlated covariance matrix for their error terms. This paper presents an algebraic approach to the statistical analysis and inference of the two correlated LRMs using some state-of-the-art formulas in linear algebra and matrix theory. It is shown first that the best linear unbiased predictors (BLUPs) of all unknown parameters under LRMs can be determined by certain linear matrix equations, and thus the BLUPs under the two LRMs can be obtained in exact algebraic expressions. We also discuss algebraical and statistical properties of the BLUPs, as well as some additive decompositions of the BLUPs. In particular, we present necessary and sufficient conditions for the separated and simultaneous BLUPs to be equivalent. The whole work provides direct access to a very simple algebraic treatment of predictors/estimators under two LRMs with correlated covariance matrices.