It is shown that the invertibility of a Toeplitz matrix can be determined through the solvability of two standard equations. The inverse matrix can be denoted as a sum of products of circulant matrices and upper triangular Toeplitz matrices. The stability of the inversion formula for a Toeplitz matrix is also considered. c © 2007 Elsevier Ltd. All rights reserved.

Some new, efficient, and numerically stable algorithms for the recursive solution of matrix problems arising in optimal beamforming and direction finding are described and analyzed. The matrix problems considered are systems of linear equations and spectral decomposition. While recursive solution procedures based on the matrix inversion lemma may be unstable, ours are stable. Furthermore, these...

The phase inversion from a morphology of hydroxypropylcellulose in water ~HPC50%! droplets in polydimethylsiloxane ~PDMS! matrix to a morphology of PDMS droplets in HPC50% matrix can be induced by a change of shear rate, due to a viscosity ratio inversion. Such a process passing through four different transient morphological stages was studied by optical microscopy in a transparent shear device...

This paper presents new algorithmic approaches and optimization techniques for LU factorization and matrix inversion of millions of very small matrices using GPUs. These problems appear in many scientific applications including astrophysics and generation of block-Jacobi preconditioners. We show that, for very small problem sizes, design and optimization of GPU kernels require a mindset differe...

The matrix inversion is an interesting topic in algebra mathematics. However, to determine an inverse matrix from a given matrix is required many computation tools and time resource if the size of matrix is huge. In this paper, we have shown an inverse closed form for an interesting matrix which has much applications in communication system. Base on this inverse closed form, the channel capacit...

Gaussian Markov random fields (GMRFs) are specified conditionally by its precision matrix meaning that its inverse, the covariance matrix, is not explicitly known. Computing the often dense covariance matrix directly using matrix inversion is often unfeasible due to time and memory requirement. In this note, we discuss a simple and fast algorithm to compute the marginal variances for a GMRF. We...

Time-Varying Lossless Systems and the Inversion of Large Structured Matrices In the inversion of large matrices, direct methods might give undesired ‘unstable’ results. Valuable insight into the mechanism of this effect is obtained by viewing the matrix as the input-output operator of a time-varying system, which allows to translate ‘unstable’ into ‘anticausal’ but bounded inverses. Inner-outer...

Computation of the inner state parameters in DSO inversion requires solving a large normal matrix system. A combined conjugate gradient and Lanczos iterative technique can be used to both solve the system and approximate some of the spectrum of the normal operator. At each iteration of the conjugate gradient algorithm, a small tridiagonal matrix (of dimension equal to the number of iterations) ...

Computation of the inner state parameters in DSO inversion requires solving a large normal matrix system. A combined conjugate gradient and Lanczos iterative technique can be used to both solve the system and approximate some of the spectrum of the normal operator. At each iteration of the conjugate gradient algorithm, a small tridiagonal matrix (of dimension equal to the number of iterations) ...