AbstractFor linear descriptor systems of the form Bẋ=Ax+Cu, y=Ox, this paper constructs reduced order systems associated with a given part of the finite spectrum of the pencil P(λ)=A−λB. It is known that the reduction can be obtained by a block diagonalization of the generalized Schur decomposition of P(λ). In this paper we consider the special case when B=[H000]and A=[JG−F∗0]. This case is suited, in particular, for linearized hydrodynamic problems. We derive a sufficient condition under which the reduced system can approximate the initial one and show that it can be obtained in significantly cheap and efficient approaches. We consider first in detail the case when F=G and H is the identity matrix and then treat the general case
AbstractWe investigate the use of inexact solves for interpolatory model reduction and consider asso...
www.mpi-magdeburg.mpg.de/preprints In this paper, we investigate interpolatory model order reduction...
AbstractConsider a generalized linear dynamical system Eẋ = Ax + Bu, where x ∈ Cn, u ∈ Cm, and E, A...
AbstractFor linear descriptor systems of the form Bẋ=Ax+Cu, y=Ox, this paper constructs reduced ord...
International audienceFor linear descriptor systems of the form Bẋ=Ax+Cu, this paper constructs red...
International audienceFor linear descriptor systems of the form Bẋ=Ax+Cu, this paper constructs red...
International audienceFor linear descriptor systems of the form Bẋ=Ax+Cu, this paper constructs red...
AbstractA model order reduction technique for systems depending on two parameters is developed. Give...
Model reduction is of fundamental importance in many control applications. We consider model reducti...
This paper studies the structure preserving (second-order to second-order) model order reduction of ...
In this report we introduce a Matlab toolbox for the regularization of descriptor systems. We apply ...
AbstractWe discuss model reduction of linear continuous-time descriptor systems that arise in the co...
We discuss model reduction of linear continuous-time descriptor systems that arise in the control of...
AbstractA simple, yet powerful approach to model order reduction of large-scale linear dynamical sys...
AbstractIn this paper, we establish a connection between Krylov subspace techniques for Multipoint P...
AbstractWe investigate the use of inexact solves for interpolatory model reduction and consider asso...
www.mpi-magdeburg.mpg.de/preprints In this paper, we investigate interpolatory model order reduction...
AbstractConsider a generalized linear dynamical system Eẋ = Ax + Bu, where x ∈ Cn, u ∈ Cm, and E, A...
AbstractFor linear descriptor systems of the form Bẋ=Ax+Cu, y=Ox, this paper constructs reduced ord...
International audienceFor linear descriptor systems of the form Bẋ=Ax+Cu, this paper constructs red...
International audienceFor linear descriptor systems of the form Bẋ=Ax+Cu, this paper constructs red...
International audienceFor linear descriptor systems of the form Bẋ=Ax+Cu, this paper constructs red...
AbstractA model order reduction technique for systems depending on two parameters is developed. Give...
Model reduction is of fundamental importance in many control applications. We consider model reducti...
This paper studies the structure preserving (second-order to second-order) model order reduction of ...
In this report we introduce a Matlab toolbox for the regularization of descriptor systems. We apply ...
AbstractWe discuss model reduction of linear continuous-time descriptor systems that arise in the co...
We discuss model reduction of linear continuous-time descriptor systems that arise in the control of...
AbstractA simple, yet powerful approach to model order reduction of large-scale linear dynamical sys...
AbstractIn this paper, we establish a connection between Krylov subspace techniques for Multipoint P...
AbstractWe investigate the use of inexact solves for interpolatory model reduction and consider asso...
www.mpi-magdeburg.mpg.de/preprints In this paper, we investigate interpolatory model order reduction...
AbstractConsider a generalized linear dynamical system Eẋ = Ax + Bu, where x ∈ Cn, u ∈ Cm, and E, A...