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.

Developed in [Deng and Lin, 2014], Least-Squares Progressive Iterative Approximation (LSPIA) is an efficient iterative method for solving B-spline curve and surface least-squares fitting systems. In [Deng and Lin 2014], it was shown that LSPIA is convergent when the iterative matrix is nonsingular. In this paper, we will show that LSPIA is still convergent even the iterative matrix is singular.

In this letter, some lower bounds for the smallest singular value of the nonsingular matrix are established. In addition, we also proposed some upper bounds on the condition number of a matrix which are the better than the bound proposed by Guggenheimer et al. [College Math. J. 26(1) (1995) 2-5]. To illustrate our bounds, some examples are also given.

An upper bound on operator norms of the adjoint matrix is presented, and special cases of the l 1 , l 2 and l 1 norms are investigated. The results are then used to obtain lower bounds on the smallest absolute value of an eigenvalue of a nonsingular matrix.

If the matrix of a square linear system is nonsingular but has very small singular values, then tiny perturbations of the right-hand side may cause drastic changes in the solution. We show that the probability for this to happen is very close to zero if sufficiently many singular values of the matrix are bounded away from zero.

A method based on the elementary operations algorithm (EOA) is developed that reduces a system matrix describing a discrete linear repetitive process to a 2-D nonsingular Roesser form such that all the input-output properties, including the transfer-function matrix, are preserved. Some areas for possible future use/application of the developed results will also be briefly discussed.

Perturbation bounds for the relative error in the eigenvalues of diagonalizable and singular matrices are derived by using perturbation theory for simple invariant subspaces of a matrix and the group inverse of a matrix. These upper bounds are supplements to the related perturbation bounds for the eigenvalues of diagonalizable and nonsingular matrices. © 2006 Elsevier Inc. All rights reserved. ...

GMRES is a popular iterative method for the solution of large linear systems of equations with a square nonsingular matrix. When the matrix is singular, GMRES may break down before an acceptable approximate solution has been determined. This paper discusses properties of GMRES solutions at breakdown and presents a modification of GMRES to overcome the breakdown.

In this paper, we present parallel alternating two-stage methods for solving linear system Ax = b, where A is a monotone matrix or an H-matrix. And we give some convergence results of these methods for nonsingular linear system. Keywords—parallel, alternating two-stage, convergence, linear system. Mathematics Subject Classification(2000) 65F10

We extend, in the context of a connected real semisimple Lie group, some results on the QR iteration and the Cholesky iteration of a nonsingular matrix. A group theoretic understanding of the abstract mechanisms of the iterations is obtained.