796 research outputs found
Relative Perturbation Theory for Quadratic Eigenvalue Problems
In this paper, we derive new relative perturbation bounds for eigenvectors
and eigenvalues for regular quadratic eigenvalue problems of the form
, where and are nonsingular
Hermitian matrices and is a general Hermitian matrix. We base our findings
on new results for an equivalent regular Hermitian matrix pair .
The new bounds can be applied to many interesting quadratic eigenvalue problems
appearing in applications, such as mechanical models with indefinite damping.
The quality of our bounds is demonstrated by several numerical experiments.Comment: 27 page
Solving rank structured Sylvester and Lyapunov equations
We consider the problem of efficiently solving Sylvester and Lyapunov
equations of medium and large scale, in case of rank-structured data, i.e.,
when the coefficient matrices and the right-hand side have low-rank
off-diagonal blocks. This comprises problems with banded data, recently studied
by Haber and Verhaegen in "Sparse solution of the Lyapunov equation for
large-scale interconnected systems", Automatica, 2016, and by Palitta and
Simoncini in "Numerical methods for large-scale Lyapunov equations with
symmetric banded data", SISC, 2018, which often arise in the discretization of
elliptic PDEs.
We show that, under suitable assumptions, the quasiseparable structure is
guaranteed to be numerically present in the solution, and explicit novel
estimates of the numerical rank of the off-diagonal blocks are provided.
Efficient solution schemes that rely on the technology of hierarchical
matrices are described, and several numerical experiments confirm the
applicability and efficiency of the approaches. We develop a MATLAB toolbox
that allows easy replication of the experiments and a ready-to-use interface
for the solvers. The performances of the different approaches are compared, and
we show that the new methods described are efficient on several classes of
relevant problems
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Nearest common root of a set of polynomials: A structured singular value approach
The paper considers the problem of calculating the nearest common root of a polynomial set under perturbations in their coefficients. In particular, we seek the minimum-magnitude perturbation in the coefficients of the polynomial set such that the perturbed polynomials have a common root. It is shown that the problem is equivalent to the solution of a structured singular value (ÎŒ) problem arising in robust control for which numerous techniques are available. It is also shown that the method can be extended to the calculation of an âapproximate GCDâ of fixed degree by introducing the notion of the generalized structured singular value of a matrix. The work generalizes previous results by the authors involving the calculation of the âapproximate GCDâ of two polynomials, although the general case considered here is considerably harder and relies on a matrix-dilation approach and several preliminary transformations
A fast solver for linear systems with displacement structure
We describe a fast solver for linear systems with reconstructable Cauchy-like
structure, which requires O(rn^2) floating point operations and O(rn) memory
locations, where n is the size of the matrix and r its displacement rank. The
solver is based on the application of the generalized Schur algorithm to a
suitable augmented matrix, under some assumptions on the knots of the
Cauchy-like matrix. It includes various pivoting strategies, already discussed
in the literature, and a new algorithm, which only requires reconstructability.
We have developed a software package, written in Matlab and C-MEX, which
provides a robust implementation of the above method. Our package also includes
solvers for Toeplitz(+Hankel)-like and Vandermonde-like linear systems, as
these structures can be reduced to Cauchy-like by fast and stable transforms.
Numerical experiments demonstrate the effectiveness of the software.Comment: 27 pages, 6 figure
New Acceleration of Nearly Optimal Univariate Polynomial Root-findERS
Univariate polynomial root-finding has been studied for four millennia and is
still the subject of intensive research. Hundreds of efficient algorithms for
this task have been proposed. Two of them are nearly optimal. The first one,
proposed in 1995, relies on recursive factorization of a polynomial, is quite
involved, and has never been implemented. The second one, proposed in 2016,
relies on subdivision iterations, was implemented in 2018, and promises to be
practically competitive, although user's current choice for univariate
polynomial root-finding is the package MPSolve, proposed in 2000, revised in
2014, and based on Ehrlich's functional iterations. By proposing and
incorporating some novel techniques we significantly accelerate both
subdivision and Ehrlich's iterations. Moreover our acceleration of the known
subdivision root-finders is dramatic in the case of sparse input polynomials.
Our techniques can be of some independent interest for the design and analysis
of polynomial root-finders.Comment: 89 pages, 5 figures, 2 table
Fast Recovery and Approximation of Hidden Cauchy Structure
We derive an algorithm of optimal complexity which determines whether a given
matrix is a Cauchy matrix, and which exactly recovers the Cauchy points
defining a Cauchy matrix from the matrix entries. Moreover, we study how to
approximate a given matrix by a Cauchy matrix with a particular focus on the
recovery of Cauchy points from noisy data. We derive an approximation algorithm
of optimal complexity for this task, and prove approximation bounds. Numerical
examples illustrate our theoretical results
Over-constrained Weierstrass iteration and the nearest consistent system
We propose a generalization of the Weierstrass iteration for over-constrained
systems of equations and we prove that the proposed method is the Gauss-Newton
iteration to find the nearest system which has at least common roots and
which is obtained via a perturbation of prescribed structure. In the univariate
case we show the connection of our method to the optimization problem
formulated by Karmarkar and Lakshman for the nearest GCD. In the multivariate
case we generalize the expressions of Karmarkar and Lakshman, and give
explicitly several iteration functions to compute the optimum.
The arithmetic complexity of the iterations is detailed
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