Two methods to decompose block matrices analogous to Singular Matrix Decomposition are proposed, one yielding the so called economy decomposition, and other yielding the full decomposition. This method is devised to avoid handling matrices bigger than the biggest blocks, so it is particularly appropriate when a limitation on the size of matrices exists. The method is tested on a document-term m...

We show that an arbitrary Hankel matrix of a nite rank admits a Vandermonde decomposition: H = V T DV , where V is a con-uent Vandermonde matrix and D is a block diagonal matrix. This result was rst derived by Vandevoorde; our contribution here is a presentation that uses only linear algebra, speciically, the Jordan canonical form. We discuss the choices for computing this decomposition in only...

The connection matrix theory for Morse decompositions is introduced. The connection matrices are matrices of maps between the homology indices of the sets in the Morse decomposition. The connection matrices cover, in a natural way, the homology index braid of the Morse decomposition and provide information about the structure of the Morse decomposition. The existence of connection matrices of M...

In this paper we present an efficient algorithm to compute the eigen decomposition of a matrix that is a weighted sum of the self outer products of vectors such as a covariance matrix of data. A well known algorithm to compute the eigen decomposition of such matrices is though the singular value decomposition, which is available only if all the weights are nonnegative. Our proposed algorithm ac...

The rank revealing URV decomposition is a useful tool in many signal processing applications that require the computation of the noise subspace of a matrix. In this paper, we look at the problem of updating the URV decomposition arid consider its implementation on a wavefront array. We prlopose a wavefront array for efficient VLSI implementation of the URV decomposition. The array provides a me...

An algorithm for decomposition of highly multicomponent signals, with variable components energy, has been proposed. The algorithm combines the singular value decomposition with the suitable time-frequency analysis approach. The auto-correlation matrix is obtained by applying the inverse form of the cross-terms free time-frequency distribution. The decomposition of the time-frequency based auto...

We show that a Schur form of a real orthogonal matrix can be obtained from a full CS decomposition. Based on this fact a CS decomposition-based orthogonal eigenvalue method is developed. We also describe an algorithm for orthogonal similarity transformation of an orthogonal matrix to a condensed product form, and an algorithm for full CS decomposition. The latter uses mixed shifted and zero-shi...

The canonical variates can be calculated from the eigenvectors of the within-group sums of squares and cross-products matrix. However, G03ACF calculates the canonical variates by means of a singular value decomposition (SVD) of a matrix V . Let the data matrix with variable (column) means subtracted be X and let its rank be k; then the k by (ng 1) matrix V is given by: V 1⁄4 QXQg; where Qg is a...

Abstract. We provide a solution to the β-Jacobi matrix model problem posed by Dumitriu and the first author. The random matrix distribution introduced here, called a matrix model, is related to the model of Killip and Nenciu, but the development is quite different. We start by introducing a new matrix decomposition and an algorithm for computing this decomposition. Then we run the algorithm on ...

A real, square matrix Q is J-orthogonal if Q JQ = J , where the signature matrix J = diag(±1). J-orthogonal matrices arise in the analysis and numerical solution of various matrix problems involving indefinite inner products, including, in particular, the downdating of Cholesky factorizations. We present techniques and tools useful in the analysis, application and construction of these matrices...