The computation of the greatest common divisor (GCD) of a set of polynomials has interested the mathematicians for a long time and has attracted a lot of attention in recent years. A challenging problem that arises from several applications, such as control or image and signal processing, is to develop a numerical GCD method that inherently has the potential to work efficiently with sets of several polynomials with inexactly known coefficients. The presented work focuses on : (i) the use of the basic principles of the ERES methodology for calculating the GCD of a set of several polynomials and defining approximate solutions by developing the hybrid implementation of this methodology. (ii) the use of the developed framework for defining the approximate notions for the GCD as a distance problem in a projective space to develop an optimization algorithm for evaluating the strength of different ad-hoc approximations derived from different algorithms. The presented new implementation of ERES is based on the effective combination of symbolic-numeric arithmetic (hybrid arithmetic) and shows interesting computational properties for the approximate GCD problem. Additionally, an efficient implementation of the strength of an approximate GCD is given by exploiting some of the special aspects of the respective distance problem. Finally, the overall performance of the ERES algorithm for computing approximate solutions is discussed.
Citation: Christou, D., Karcanias, N. & Mitrouli, M. (2014). Matrix representation of the shifting operation and numerical properties of the ERES method for computing the greatest common divisor of sets of many polynomials. Journal of Computational and Applied Mathematics, 260, pp. 54-67. doi: 10.1016/j.cam.2013.09.021 This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent AbstractThe Extended-Row-Equivalence and Shifting (ERES) method is a matrixbased method developed for the computation of the greatest common divisor (GCD) of sets of many polynomials. In this paper we present the formulation of the shifting operation as a matrix product which allows us to study the fundamental theoretical and numerical properties of the ERES method by introducing its complete algebraic representation. Then, we analyse in depth its overall numerical stability in finite precision arithmetic. Numerical examples and comparison with other methods are also presented.
This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe computation of the Greatest Common Divisor (GCD) of a set of polynomials is an important issue in computational mathematics and it is linked to Control Theory very strong. In this paper we present different matrix-based methods, which are developed for the efficient computation of the GCD of several polynomials. Some of these methods are naturally developed for dealing with numerical inaccuracies in the input data and produce meaningful approximate results. Therefore, we describe and compare numerically and symbolically methods such as the ERES, the Matrix Pencil and other resultant type methods, with respect to their complexity and effectiveness. The combination of numerical and symbolic operations suggests a new approach in software mathematical computations denoted as hybrid computations. This combination offers great advantages, especially when we are interested in finding approximate solutions. Finally the notion of approximate GCD is discussed and a useful criterion estimating the strength of a given approximate GCD is also developed.
The computation of the Greatest Common Divisor (GCD) of a set of more than two polynomials is a non-generic problem. There are cases where iterative methods of computing the GCD of many polynomials, based on the Euclidean algorithm, fail to produce accurate results, when they are implemented in a software programming environment. This phenomenon is very strong especially when floating-point data are being used. The ERES method is an iterative matrix based method, which successfully evaluates an approximate GCD, by performing row transformations and shifting on a matrix, formed directly from the coefficients of the given polynomials. ERES deals with any kind of real data. However, due to its iterative nature, it is extremely sensitive when performing floating-point operations. It succeeds in producing results with minimal error, if we combine both floating-point and symbolic operations. In the present paper we study the behavior of the ERES method using floating-point and exact symbolic arithmetic. The conclusions derived from our study are useful for any other algorithm involving extended matrix operations.
This is the accepted version of the paper.This version of the publication may differ from the final published version. (e-mail: N.Karcanias@city.ac.). Permanent Abstract:The paper is concerned with establishing the links between the approximate GCD of a set of polynomials and the notion of the pseudo-spectrum defined on a set of polynomials. By examining the pseudo-spectrum of the structured matrix we will derive estimates of the area of the approximate roots of the initial polynomial set. We will relate the strength of the GCD to the weighted strength of the pseudospectra and we investigate under which conditions the roots of the approximate GCDs are a subset of the pseudo-spectra.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.