An ODE-Based Method for Computing the Distance of Coprime Polynomials to Common Divisibility

The problem of computing the distance of two real coprime polynomials to the set of polynomials with a nontrivial greatest common divisor (GCD) appears in computer algebra, signal processing, and control theory. It has been studied in the literature under the names approximate common divisor, $\varepsilon$-GCD, and distance to uncontrollability. Existing solution methods use different types of local optimization methods and require a user-defined initial approximation. In this paper, we propose a new method that allows us to include constraints on the coefficients of the polynomials. Moreover, the method proposed in the paper is more robust to the initial approximation than the Newton-type optimization methods available in the literature. Our approach consists of two steps: (1) reformulate the problem as a problem of determining the structured distance to singularity of an associated Sylvester matrix, and (2) integrate a system of ODEs, which describes the gradient associated to the functional to be minim...

[1]  George Labahn,et al.  When are Two Numerical Polynomials Relatively Prime? , 1998, J. Symb. Comput..

[2]  Ivan Markovsky,et al.  Variable projection methods for approximate (greatest) common divisor computations , 2017, Theor. Comput. Sci..

[3]  Zhonggang Zeng,et al.  The numerical greatest common divisor of univariate polynomials , 2021, ArXiv.

[4]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[5]  Stephen M. Watt,et al.  The singular value decomposition for polynomial systems , 1995, ISSAC '95.

[6]  Zhonggang Zeng,et al.  The approximate GCD of inexact polynomials , 2004, ISSAC '04.

[7]  J. Sylvester,et al.  XVIII. On a theory of the syzygetic relations of two rational integral functions, comprising an application to the theory of Sturm’s functions, and that of the greatest algebraical common measure , 1853, Philosophical Transactions of the Royal Society of London.

[8]  Ivan Markovsky,et al.  Structured low-rank approximation and its applications , 2008, Autom..

[9]  Michael L. Overton,et al.  An Efficient Algorithm for Computing the Generalized Null Space Decomposition , 2015, SIAM J. Matrix Anal. Appl..

[10]  Nicola Guglielmi,et al.  Computing the Structured Pseudospectrum of a Toeplitz Matrix and Its Extreme Points , 2012, SIAM J. Matrix Anal. Appl..

[11]  N. Higham MATRIX NEARNESS PROBLEMS AND APPLICATIONS , 1989 .

[12]  Thomas Kailath,et al.  Linear Systems , 1980 .

[13]  Ivan Markovsky,et al.  Optimization on a Grassmann manifold with application to system identification , 2014, Autom..

[14]  Erich Kaltofen,et al.  Challenges of Symbolic Computation: My Favorite Open Problems , 2000, J. Symb. Comput..

[15]  Ivan Markovsky,et al.  Software for weighted structured low-rank approximation , 2014, J. Comput. Appl. Math..

[16]  Ivan Markovsky,et al.  Structured Low-Rank Approximation with Missing Data , 2013, SIAM J. Matrix Anal. Appl..

[17]  Tosio Kato Perturbation theory for linear operators , 1966 .

[18]  I. Emiris,et al.  Certified approximate univariate GCDs , 1997 .

[19]  G. Golub,et al.  Separable nonlinear least squares: the variable projection method and its applications , 2003 .

[20]  Nicola Guglielmi,et al.  Differential Equations for Roaming Pseudospectra: Paths to Extremal Points and Boundary Tracking , 2011, SIAM J. Numer. Anal..

[21]  M. Overton,et al.  FAST APPROXIMATION OF THE H∞ NORM VIA OPTIMIZATION OVER SPECTRAL VALUE SETS∗ , 2012 .

[22]  Robert E. Mahony,et al.  Optimization Algorithms on Matrix Manifolds , 2007 .

[23]  Michael L. Overton,et al.  Fast Algorithms for the Approximation of the Pseudospectral Abscissa and Pseudospectral Radius of a Matrix , 2011, SIAM J. Matrix Anal. Appl..

[24]  Erich Kaltofen,et al.  Approximate greatest common divisors of several polynomials with linearly constrained coefficients and singular polynomials , 2006, ISSAC '06.

[25]  Ivan Markovsky,et al.  Variable projection for affinely structured low-rank approximation in weighted 2-norms , 2014, J. Comput. Appl. Math..

[26]  Stephen P. Boyd,et al.  Branch and bound algorithm for computing the minimum stability degree of parameter-dependent linear systems , 1991, International Journal of Robust and Nonlinear Control.

[27]  G. Zoutendijk,et al.  Mathematical Programming Methods , 1976 .

[28]  Nicola Guglielmi,et al.  Low-Rank Dynamics for Computing Extremal Points of Real Pseudospectra , 2013, SIAM J. Matrix Anal. Appl..

[29]  Nicola Guglielmi,et al.  Approximating real stability radii , 2015 .

[30]  Ivan Markovsky,et al.  Factorization Approach to Structured Low-Rank Approximation with Applications , 2014, SIAM J. Matrix Anal. Appl..

[31]  Narendra Karmarkar,et al.  On Approximate GCDs of Univariate Polynomials , 1998, J. Symb. Comput..

[32]  Adrian S. Lewis,et al.  Pseudospectral Components and the Distance to Uncontrollability , 2005, SIAM J. Matrix Anal. Appl..

[33]  Dario Bini,et al.  A Fast Algorithm for Approximate Polynomial GCD Based on Structured Matrix Computations , 2010 .

[34]  Akira Terui An iterative method for calculating approximate GCD of univariate polynomials , 2009, ISSAC '09.

[35]  Jeremy F Shapiro Mathematical Programming Methods for Logistics Planning. , 1981 .

[36]  Robert M. Corless,et al.  Optimization Strategies for the Floating-point Gcd , 1998 .

[37]  Michael L. Overton,et al.  Fast Approximation of the HINFINITY Norm via Optimization over Spectral Value Sets , 2013, SIAM J. Matrix Anal. Appl..

[38]  Edward J. Davison,et al.  Real controllability/stabilizability radius of LTI systems , 2004, IEEE Transactions on Automatic Control.

[39]  Antonis I. G. Vardulakis,et al.  Generalized Resultant Theorem , 1978 .

[40]  Dario Bini,et al.  Structured matrix-based methods for polynomial ∈-gcd: analysis and comparisons , 2007, ISSAC '07.

[41]  Thomas W. Sederberg,et al.  Best linear common divisors for approximate degree reduction , 1993, Comput. Aided Des..

[42]  L. Trefethen,et al.  Spectra and Pseudospectra , 2020 .

[43]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[44]  I. Markovsky A software package for system identification in the behavioral setting , 2013 .

[45]  David Rupprecht An algorithm for computing certified approximate GCD of n univariate polynomials , 1999 .

[46]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[47]  Silvia Noschese,et al.  Eigenvalue patterned condition numbers: Toeplitz and Hankel cases , 2007 .