A singular value decomposition updating algorithm for subspace tracking


26-Mar-2019 13:58

Generalised eigen- and singular value decompositions can also be understood in this framework. There are a number of reasons why this is a good thing to do.

Relationships between these methods, and their accuracy, is discussed. Product algorithms are algorithms to compute factorisations of products of matrices that works with the product in terms of its factors.

The third approach seeks to combine the best attributes of the first two.

Contents 1 Introduction and problem statement 1 2 Motivating Examples 3 3 Approximation methods 4 3.1 SVD-based approximation methods .

In this paper we review the state of affairs in the area of approximation of large-scale systems.

We distinguish among three basic categories, namely the SVD-based, the Krylov-based and the SVD-Krylov-based approximation methods.

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Most available computationally efficient subspace tracking algorithms rely on off-line estimation of the signal subspace dimension, which acts as a bottleneck in real-time parallel implementations.

The most recent work by Bai and Demmel [7], and Adams, Bojanczyk, Ewerbring, Lu... We discuss a number of novel issues in the interdisciplinary area of numerical linear algebra and control theory.