2004
DOI: 10.1016/s0096-3003(03)00768-9
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Partitioning method for rational and polynomial matrices

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Cited by 23 publications
(21 citation statements)
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“…Wang in [26] generalizes Greville's method to the weighted Moore-Penrose inverse. The algorithm for computing the Moore-Penrose inverse of one-variable polynomial and/or rational matrices, based on the Greville's partitioning algorithm, is established in [19]. An extension of the results from [19] to the set of two-variable rational and polynomial matrices is introduced in the paper [18].…”
Section: Preliminaries and Motivationmentioning
confidence: 99%
See 1 more Smart Citation
“…Wang in [26] generalizes Greville's method to the weighted Moore-Penrose inverse. The algorithm for computing the Moore-Penrose inverse of one-variable polynomial and/or rational matrices, based on the Greville's partitioning algorithm, is established in [19]. An extension of the results from [19] to the set of two-variable rational and polynomial matrices is introduced in the paper [18].…”
Section: Preliminaries and Motivationmentioning
confidence: 99%
“…The algorithm for computing the Moore-Penrose inverse of one-variable polynomial and/or rational matrices, based on the Greville's partitioning algorithm, is established in [19]. An extension of the results from [19] to the set of two-variable rational and polynomial matrices is introduced in the paper [18]. In the paper [22] we extended the Wang's partitioning method from [26] to the set of one-variable rational and polynomial matrices.…”
Section: Preliminaries and Motivationmentioning
confidence: 99%
“…Grevile in [11] proposed a recursive algorithm which relates the MoorePenrose pseudoinverse of a matrix R augmented by an appropriate vector r with the pseudoinverse R † of R. A generalization of this statement, which is applicable to rational matrices and its implementation in the package MATHE-MATICA is presented in [24]. In the present paper we use this implementation in the pseudoinverse computation.…”
Section: Introductionmentioning
confidence: 99%
“…A generalization of the generalized inverse is the weighted Moore-Penrose inverse of an arbitrary matrix which has many applications in numerical computation, statistics, prediction theory, control systems and analysis and curve fitting, see e.g., [1,9,14]. There have been many numerical methods for the computation of the weighted MoorePenrose inverse, see e.g., [6,7,10,11]. It is an interesting problem to determine how the weighted Moore-Penrose inverse is transformed under perturbation.…”
mentioning
confidence: 99%