We consider second-order linear time-invariant systems. The objective of this paper is to present a new method for constructing a reduced system by preserving the second-order structure of the… (More)

In this paper we show how to compute recursively an approximation of the left and right dominant singular subspaces of a given matrix. In order to perform as few as possible operations on each column… (More)

In this chapter, the problem of constructing a reduced order system while preserving the second order structure of the original system is discussed. After a brief introduction on second order systems… (More)

This paper presents new recursive projection techniques to compute reduced order models of time-varying linear systems. The methods produce a lowrank approximation of the Gramians or of the Hankel… (More)

In this paper we present a Smith-like updating technique to estimate a low rank approximation of the Gramians of a time-varying system. We obtain error estimates of our approximation and also explain… (More)

In this paper, we describe an algorithm for estimating the H∞-norm of a large linear time invariant dynamical system described by a discrete time state-space model. The algorithm is designed to be… (More)

In the last twenty years, model reduction of large scale dynamical systems has become very popular. The idea is to construct a “simple” lower order model that approximates well the behavior of the… (More)

In this note we present a new updating technique to estimate a low rank approximation of the Hankel map of a time-varying system. We obtain error estimates of our approximation and also explain how… (More)

We describe an algorithm for estimating the H∞-norm of a large linear time invariant dynamical system described by a discrete time state-space model. The algorithm uses Chandrasekhar iterations to… (More)