We show spectroscopic Mueller-matrix data measured at multiple incidence angles of the scarab beetle C. aurata. A method of regression decomposition can decompose the Mueller matrix into a set of two matrices representing one polarizer and one dielectric reflector. We also report on a tentative decomposition of the beetle C. argenteola using the same method.

Different approaches to the decomposition of a nonsingular totally positive matrix as a product of bidiagonal matrices are studied. Special attention is paid to the interpretation of the factorization in terms of the Neville elimination process of the matrix and in terms of corner cutting algorithms of Computer Aided Geometric Design. Conditions of uniqueness for the decomposition are also given.

We demonstrate that substantial computational savings are attainable in electronic structure calculations using a Cholesky decomposition of the two-electron integral matrix. In most cases, the computational effort involved calculating the Cholesky decomposition is less than the construction of one Fock matrix using a direct O(N) procedure. © 2003 American Institute of Physics. @DOI: 10.1063/1.1...

We propose a novel network traffic matrix decomposition method named Stable Principal Component Pursuit with FrequencyDomain Regularization (SPCP-FDR), which improves the Stable Principal Component Pursuit (SPCP) method by using a frequency-domain noise regularization function. An experiment demonstrates the feasibility of this new decomposition method. key words: Traffic Matrix, Stable Princip...

We consider the computation of the Iwasawa decomposition of a symplectic matrix via the QR factorization. The algorithms presented improve on the method recently described by T.-Y. Tam in [Computing Iwasawa decomposition of a symplectic matrix by Cholesky factorization, Appl. Math. Lett. (in press) doi:10.1016/j.aml.2006.03.001]. c © 2006 Elsevier Ltd. All rights reserved.

abstract The singular value decomposition has a number of applications in digital signal processing. However, the the decomposition must be computed from a matrix consisting of both signal and noise. It is therefore important to be able to assess the eeects of the noise on the singular values and singular vectors | a problem in classical perturbation theory. In this paper we survey the perturba...

In this paper, we propose a novel algorithm for underdetermined blind identification problems in blind signal separation. The proposed algorithm is based on the charrelation matrix of observations. The charrelation matrix can not only be considered as a generalized covariance matrix, but also incorporates higher-order information. It is significant for blind separation problem based on statisti...

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)...

A scaled version of the lower and the upper triangular factors of the inverse of the Vandermonde matrix is given. Two applications of the q-Pascal matrix resulting from the factorization of the Vandermonde matrix at the q-integer nodes are introduced. c © 2007 Elsevier Ltd. All rights reserved.