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A method for the computation ofinverses of a given matrixis derived, based on the full-rankdecomposition of an appropriate matrix. As a corollary, a new method considering the advantages of full-rankdecomposition is developed. It is then specialized to the set of polynomial matrices. Therefore, an algorithm for efficient symbolic computation ofinverses of a polynomial matrix is proposed. An additional diagonal matrixyields to avoiding the computation of entries containing square roots of polynomials, therefore increasing the algorithm’s performances. Some implementation details and comparative processing times to other similar methods are provided, illustrating the algorithm’s efficiency. [ABSTRACT FROM AUTHOR]

We introduce a simultaneous decomposition for a matrix triplet ( A, B, C), where A=± A and (⋅) denotes the conjugate transpose of a matrix, and use the simultaneous decomposition to solve some conjectures on the maximal and minimal values of the ranks of the matrix expressions A− BXC±( BXC) with respect to a variable matrix X. In addition, we give some explicit formulas for the maximal and minimal values of the inertia of the matrix expression A− BXC−( BXC) with respect to X. As applications, we derive the extremal ranks and inertias of the matrix expression D− CXC subject to Hermitian solutions of a consistent matrix equation AXA= B, as well as the extremal ranks and inertias of the Hermitian Schur complement D− B A B with respect to a Hermitian generalized inverse A of A. Various consequences of these extremal ranks and inertias are also presented in the paper. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Suppose A-BXB*, A-BX -X*B*, and A-BX +X*B* are three linear matrix expressions over the field of complex numbers, where A is an Hermitian or skew-Hermitian matrix. In this paper, we consider how to choose an Hermitian or skew-Hermitian matrix X such that A-BXB* have the maximal and minimal possible ranks, and how to choose X such that A - BX ± X*B* attain the minimal possible ranks. Some applications to Hermitian or skew-Hermitian solutions of matrix equations with symmetric patterns are also given. [ABSTRACT FROM AUTHOR]