Name
Title | ||
|---|---|---|
Generalized algorithms for the approximate matrix polynomial GCD of reducing data uncertainties with application to MIMO system and control |
Abstract | ||
|---|---|---|
Computation of (approximate) polynomials common factors is an important problem in several fields of science, like control theory and signal processing. While the problem has been widely studied for scalar polynomials, the scientific literature in the framework of matrix polynomials seems to be limited to the problem of exact greatest common divisor computation. In this paper, we generalize two algorithms from scalar to matrix polynomials. The first one is fast and simple. The second one is more accurate but computationally more expensive. We test the performances of the two algorithms and observe similar behavior to the one in the scalar case. Finally we describe an application to multi-input multi-output linear time-invariant dynamical systems. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.cam.2021.113499 | Journal of Computational and Applied Mathematics |
Keywords | DocType | Volume |
Matrix polynomials,Approximate common factor,Subspace method,Matrix ODEs | Journal | 393 |
ISSN | Citations | PageRank |
0377-0427 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Fazzi | 1 | 0 | 0.34 |
| Nicola Guglielmi | 2 | 0 | 0.34 |
| Ivan Markovsky | 3 | 427 | 43.02 |