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Journal ArticleDOI

Adaptive Tangential Interpolation in Rational Krylov Subspaces for MIMO Dynamical Systems

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TLDR
An effective way to treat multiple inputs by dynamically choosing the next direction vectors to expand the space is presented and is competitive with respect to state-of-the-art methods both in terms of CPU time and memory requirements.
Abstract
Model reduction approaches have been shown to be powerful techniques in the numerical simulation of very large dynamical systems. The presence of multiple inputs and outputs (MIMO systems) makes the reduction process even more challenging. We consider projection-based approaches where the reduction of complexity is achieved by direct projection of the problem onto a rational Krylov subspace of significantly smaller dimension. We present an effective way to treat multiple inputs by dynamically choosing the next direction vectors to expand the space. We apply the new strategy to the approximation of the transfer matrix function and to the solution of the Lyapunov matrix equation. Numerical results confirm that the new approach is competitive with respect to state-of-the-art methods both in terms of CPU time and memory requirements.

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Citations
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Journal ArticleDOI

Computational Methods for Linear Matrix Equations

Valeria Simoncini
- 04 Aug 2016 - 
TL;DR: The aim is to provide an overview of the major algorithmic developments that have taken place over the past few decades in the numerical solution of this and related problems, which are producing reliable numerical tools in the formulation and solution of advanced mathematical models in engineering and scientific computing.
Journal ArticleDOI

The RKFIT algorithm for nonlinear rational approximation

TL;DR: This paper derives a strategy for the degree reduction of the approximants, as well as methods for their conversion to partial fraction form, for the efficient evaluation, and root-finding, and puts RKFIT into a general framework.
Journal ArticleDOI

Frequency-Limited Balanced Truncation with Low-Rank Approximations

TL;DR: It is shown in further numerical examples that frequency-limited balanced truncation generates reduced order models which are significantly more accurate in the considered frequency region.
Journal ArticleDOI

Efficient low-rank solution of generalized Lyapunov equations

TL;DR: Numerical experiments indicate that this iterative method for the low-rank approximate solution of a class of generalized Lyapunov equations is competitive vis-à-vis the current state-of-the-art methods, both in terms of computational times and storage needs.

Efficient Low-Rank Solution of Large-Scale Matrix Equations

TL;DR: Improved low-rank ADI methods for Lyapunov and Sylvester equations are used in Newton type methods for finding approximate solutions of quadratic matrix equations in the form of symmetric, continuous-time, but also more general nonsymmetric, algebraic Riccati equations.
References
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Book

Iterative Methods for Sparse Linear Systems

Yousef Saad
TL;DR: This chapter discusses methods related to the normal equations of linear algebra, and some of the techniques used in this chapter were derived from previous chapters of this book.
Book

Approximation of Large-Scale Dynamical Systems

TL;DR: This paper presents SVD-Krylov Methods and Case Studies, a monograph on model reduction using Krylov methods for linear dynamical systems, and some examples of such reduction schemes.
Dissertation

Krylov Projection Methods for Model Reduction

TL;DR: The cornerstone of this dissertation is a collection of theory relating Krylov projection to rational interpolation, based on which three algorithms for model reduction are proposed, which are suited for parallel or approximate computations.
Book

Interpolation of Rational Matrix Functions

TL;DR: In this paper, the authors present a formal notation for solving homogeneous and non-homogeneous interpolation problems for matrix functions with J-unitary values on the Imaginary Axis or Unit Circle.
Journal ArticleDOI

$\mathcal{H}_2$ Model Reduction for Large-Scale Linear Dynamical Systems

TL;DR: A new unifying framework for the optimal $\mathcal{H}_2$ approximation problem is developed using best approximation properties in the underlying Hilbert space and leads to a new set of local optimality conditions taking the form of a structured orthogonality condition.