2002Unpublished venueRequires access

Krylov subspace methods in control: an overview

Biswa Nath Datta

Open publisher page 4 citations

Abstract

There have been some developments in the area of large and sparse matrix computations. A class of classical methods known as the Krylov subspace methods that include the Lanczos and Arnoldi methods, have been found to be suitable for sparse matrix computations. We give a brief overview of some of the recently developed Arnoldi and Lanczos based methods that seem to be suitable for large and sparse control problems. The research in this area is still in its infancy.

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What this paper is about

There have been some developments in the area of large and sparse matrix computations. A class of classical methods known as the Krylov subspace methods that include the Lanczos and Arnoldi methods, have been found to be suitable for sparse matrix computations. We give a brief overview of some of the recently developed Arnoldi and Lanczos based methods that seem to be suitable for large and sparse control problems. The research in this area is still in its infancy.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

There have been some developments in the area of large and sparse matrix computations. A class of classical methods known as the Krylov subspace methods that include the Lanczos and Arnoldi methods, have been found to be suitable for sparse matrix computations. We give a brief overview of some of the recently developed Arnoldi and Lanczos based methods that seem to be suitable for large and sparse control problems. The research in this area is still in its infancy.

Key concepts: Krylov subspace, Lanczos resampling, Lanczos algorithm, Sparse matrix, Computation, Computer science, Generalized minimal residual method, Arnoldi iteration

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