Concept

Divide-and-conquer eigenvalue algorithm

Publications associées (47)

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Structure-preserving eigenvalue solvers for robust stability and controllability estimates

Daniel Kressner

Structured eigenvalue problems feature a prominent role in many algorithms for the computation of robust measures for the stability or controllability of a linear control system. Structures that typically arise are Hamiltonian, skew-Hamiltonian, and symple ...

IEEE2006

New Hamiltonian eigensolvers with applications in control

Daniel Kressner

A new MATLAB toolbox for computing eigenvalues and invariant subspaces of Hamiltonian and skew-Hamiltonian matrices is described. Based on orthogonal symplectic decompositions, the implemented algorithms are both numerically backward stable and structure-p ...

2005

Zeros of Continuous-time Linear Periodic Systems

Giancarlo Ferrari Trecate

Zeros of continuous-time linear periodic systems are defined and their properties investigated. Under the assumption that the system has uniform relative degree, the zero-dynamics of the system is characterized and a closed-form expression of the blocking ...

1998

Zeros of Continuous-time Linear Periodic Systems

Giancarlo Ferrari Trecate

1996

Tunable Integration Scheme for the Finite-Element Method

A discretization method is proposed where a tunable integration scheme is applied to the finite element method (FEM). The method is characterized by one continuous parameter, p. A theoretical error analysis is given and three different eigenvalue problems ...

1991

A subspace forward iteration method for solving the quadratic eigenproblem associated with the FDE formulation

Thomas Gmür

This paper presents the development of an efficient and numerically stable algorithm for accurately computing the eigensolutions of the quadratic real symmetric eigenproblem arising in the finite dynamic element (FDE) formulation. Closely related to the su ...

1990

Fast Eigenspace Approximation using Random Signals

Lionel Jérémie Martin, Johann Paratte

We focus in this work on the estimation of the first k eigenvectors of any graph Laplacian using filtering of Gaussian random signals. We prove that we only need k such signals to be able to exactly recover as many of the smallest eigenvectors, regardless ...