Efficient Ehrlich–Aberth iteration for finding intersections of interpolating polynomials and rational functions

Abstract We analyze the problem of carrying out an efficient iteration to approximate the eigenvalues of some rank structured pencils obtained as linearization of sums of polynomials and rational functions expressed in (possibly different) interpolation bases. The class of linearizations that we consider has been introduced by Robol, Vandebril and Van Dooren in [17] . We show that a traditional QZ iteration on the pencil is both asymptotically slow (since it is a cubic algorithm in the size of the matrices) and sometimes not accurate (since in some cases the deflation of artificially introduced infinite eigenvalues is numerically difficult). To solve these issues we propose to use a specifically designed Ehrlich–Aberth iteration that can approximate the eigenvalues in O ( k n 2 ) flops, where k is the average number of iterations per eigenvalue, and n the degree of the linearized polynomial. We suggest possible strategies for the choice of the initial starting points that make k asymptotically smaller than O ( n ) , thus making this method less expensive than the QZ iteration. Moreover, we show in the numerical experiments that this approach does not suffer of numerical issues, and accurate results are obtained.

[1]  Oliver Aberth,et al.  Iteration methods for finding all zeros of a polynomial simultaneously , 1973 .

[2]  J. Walsh Interpolation and Approximation by Rational Functions in the Complex Domain , 1935 .

[3]  William H. Gustafson A note on matrix inversion , 1984 .

[4]  Lloyd N. Trefethen,et al.  Barycentric Lagrange Interpolation , 2004, SIAM Rev..

[5]  Edmund Taylor Whittaker,et al.  The Calculus of Observations. , 1924 .

[6]  L. Rodman,et al.  Interpolation of Rational Matrix Functions , 1990 .

[7]  S. Liberty,et al.  Linear Systems , 2010, Scientific Parallel Computing.

[8]  A. Brauer Limits for the characteristic roots of a matrix. III , 1946 .

[9]  Dario Bini,et al.  Journal of Computational and Applied Mathematics Solving secular and polynomial equations: A multiprecision algorithm , 2022 .

[10]  Marcel Boumans,et al.  Calculus of Observations , 2015 .

[11]  Louis W. Ehrlich,et al.  A modified Newton method for polynomials , 1967, CACM.

[12]  A. Edelman,et al.  Polynomial roots from companion matrix eigenvalues , 1995 .

[13]  Dario Bini,et al.  The Ehrlich-Aberth Method for the Nonsymmetric Tridiagonal Eigenvalue Problem , 2005, SIAM J. Matrix Anal. Appl..

[14]  Wim Michiels,et al.  Linearization of Lagrange and Hermite interpolating matrix polynomials , 2015 .

[15]  Lloyd N. Trefethen,et al.  The Exponentially Convergent Trapezoidal Rule , 2014, SIAM Rev..

[16]  A. B. Farnell Limits for the characteristic roots of a matrix , 1944 .

[17]  Jr. G. Forney,et al.  Minimal Bases of Rational Vector Spaces, with Applications to Multivariable Linear Systems , 1975 .

[18]  Paul Van Dooren,et al.  A Framework for Structured Linearizations of Matrix Polynomials in Various Bases , 2016, SIAM J. Matrix Anal. Appl..

[19]  Gerald E. Farin,et al.  Curves and surfaces for computer-aided geometric design - a practical guide, 4th Edition , 1997, Computer science and scientific computing.

[20]  Dario A. Bini,et al.  Solving polynomial eigenvalue problems by means of the Ehrlich-Aberth method , 2012, 1207.6292.

[21]  E. Ecer,et al.  Numerical Linear Algebra and Applications , 1995, IEEE Computational Science and Engineering.

[22]  Frann Coise Tisseur Backward Error and Condition of Polynomial Eigenvalue Problems , 1999 .

[23]  Henrici Computational complex analysis , 1973 .

[24]  Dario Bini,et al.  Generalization of the Brauer Theorem to Matrix Polynomials and Matrix Laurent Series , 2015, 1512.07118.