In recent years, reduced-order modelling techniques based on Krylov-subspace iterations,
especially the Lanczos algorithm and the Arnoldi process, have become popular tools for …

In many large scale scientific or engineering computations, ranging from computing the
frequency response of a circuit to the earthquake response of a buildingto the energy levels …

The Handbook of Linear Algebra provides comprehensive coverage of linear algebra
concepts, applications, and computational software packages in an easy-to-use handbook …

The past few decades have seen a significant spurt in develo** lower-order, parsimonious
models of large-scale dynamical systems used for design and control. These surrogate …

The simulation of electronic circuits involves the numerical solution of very large-scale,
sparse, in general nonlinear, systems of differential-algebraic equations. Often, the size of …

In recent years, order-reduction techniques based on Krylov subspaces have become the
methods of choice for generating macromodels of large multi-port RLC circuits. A widely …

In this first section we present a high level discussion on computational science, and the
need for compact models of phenomena observed in nature and industry. We argue that …

In this paper, we propose a model reduction algorithm for approximation of large-scale
linear time-invariant dynamical systems. The method is a two-sided projection combining …

Solving systems of algebraic linear equations is among the most frequent problems in
scientific computing. It appears in many areas like physics, engineering, chemistry, biology …

For reduced order modelling of large scale second order systems while preserving the
second order structure, we propose two approaches based on matching some of the …