In this paper we consider random block matrices, which generalize the general beta ensembles, which were recently investigated by Dumitriu and Edelmann (2002, 2005). We demonstrate that the eigenvalues of these random matrices can be uniformly approximated by roots of matrix orthogonal polynomials which were investigated independently from the random matrix literature. As a consequence we deriv...

Some block Toeplitz methods applied to polynomial matrices are reviewed. We focus on the computation of the structure (rank, null-space, infinite and finite structures) of an arbitrary rectangular polynomial matrix. We also introduce some applications of this structural information in control theory. All the methods outlined here are based on the computation of the null-spaces of suitable block...

Efficiently manipulating and operating on block matrices can be beneficial in many applications, among others those involving iteratively solving nonlinear systems. These types of problems consist of repeatedly assembling and solving sparse linear systems. In the case of very large systems, without a careful manipulation of the corresponding matrices, solving can become very time consuming. Thi...

This paper introduces a novel method based on auxiliary matrix to hide a text data in an RGB plane. To hide the data in RGB planes of image via scanning, encryption and decryption. To enhance the security, the scanning technique combines two different traversals – spiral and snake traversal. The encryption algorithm involves auxiliary matrix as a payload and consider the least significant bits ...

One sided block Jacobi algorithm for the singular value decomposition (SVD) of matrix can be a method of choice to compute SVD efficiently and accurately in parallel. A given matrix is logically partitioned into block columns and is subjected to an iteration process. In each iteration step, for given two block columns, their Gram matrix is generated, its symmetric eigenvalue decomposition (EVD)...

We investigate special cases of the quadratic assignment problem (QAP) where one of the two underlying matrices carries a simple block structure. For the special case where the second underlying matrix is a monotone anti-Monge matrix, we derive a polynomial time result for a certain class of cut problems. For the special case where the second underlying matrix is a product matrix, we identify t...

In this paper we define multiplication between intuitionistic fuzzy matrices (IFMs) and we derive the conditions for a block IFM to be regular. Also a method to find the generalized inverse of it with the help of the generalized inverses of the blocks of the original matrix is described. Again, it is shown that a block intuitionistic fuzzy matrix can be decomposed into an upper triangular idemp...

In this paper we introduce a vector ISA extension to facilitate sparse matrix manipulation on vector processors (VPs). First we introduce a new Block Based Compressed Storage (BBCS) format for sparse matrix representation and a Block-wise Sparse Matrix-Vector Multiplication approach. Additionally, we propose two vector instructions, Multiple Inner Product and Accumulate (MIPA) and LoaD Section ...

Every n×n generalized K-centrosymmetric matrix A can be reduced into a 2× 2 block diagonal matrix (see [20]). This block diagonal matrix is called the reduced form of the matrix A. In this paper we further investigate some properties of the reduced form of these matrices and discuss the square roots of these matrices. Finally exploiting these properties, the development of structure-preserving ...

In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more components at once, respectively. While the initial formulations involve nonconvex functions, and are therefore computa...