Annals of Functional Analysis

Preconditioners in spectral approximation

V. B. Kiran Kumar

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Abstract

Let H be a complex separable Hilbert space, and let A be a bounded self-adjoint operator on H. Consider the orthonormal basis B={e1,e2,} and the projection Pn of H onto the finite-dimensional subspace spanned by the first n elements of B. The finite-dimensional truncations An=PnAPn shall be regarded as a sequence of finite matrices by restricting their domains to Pn(H) for each n. Many researchers used the sequence of eigenvalues of An to obtain information about the spectrum of A. But in many situations, these An’s need not be simple enough to make the computations easier. The natural question Can we use some simpler sequence of matrices Bn instead of An? is addressed in this article. The notion of preconditioners and their convergence in the sense of eigenvalue clustering are used to study the problem. The connection between preconditioners and compact perturbations of operators is identified here. The usage of preconditioners in the spectral gap prediction problems is also discussed. The examples of Toeplitz and block Toeplitz operators are considered as an application of these results. Finally, some future possibilities are discussed.

Article information

Source
Ann. Funct. Anal., Volume 7, Number 2 (2016), 326-337.

Dates
Received: 29 April 2015
Accepted: 20 August 2015
First available in Project Euclid: 8 April 2016

Permanent link to this document
https://projecteuclid.org/euclid.afa/1460141558

Digital Object Identifier
doi:10.1215/20088752-3506079

Mathematical Reviews number (MathSciNet)
MR3484386

Zentralblatt MATH identifier
1341.47003

Subjects
Primary: 47A10: Spectrum, resolvent
Secondary: 47B15: Hermitian and normal operators (spectral measures, functional calculus, etc.) 47B35: Toeplitz operators, Hankel operators, Wiener-Hopf operators [See also 45P05, 47G10 for other integral operators; see also 32A25, 32M15]

Keywords
spectrum preconditioners truncation

Citation

Kumar, V. B. Kiran. Preconditioners in spectral approximation. Ann. Funct. Anal. 7 (2016), no. 2, 326--337. doi:10.1215/20088752-3506079. https://projecteuclid.org/euclid.afa/1460141558


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