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μ -Hankel operators on Hilbert spaces

Mirotin Adolf, Kuzmenkova Ekaterina

Opuscula Mathematica

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2021

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Vol. 41, no. 6

881--898

EN

A class of operators is introduced (μ -Hankel operators, μ is a complex parameter), which generalizes the class of Hankel operators. Criteria for boundedness, compactness, nuclearity, and finite dimensionality are obtained for operators of this class, and for the case |μ| = 1 their description in the Hardy space is given. Integral representations of ^-Hankel operators on the unit disk and on the Semi-Axis are also considered.

2

Product of weighted Hankel and weighted Toeplitz operators

Datt G., Porwal D. K.

Demonstratio Mathematica

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2013

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Vol. 46, nr 3

571--583

EN

In this paper, we discuss some properties of the weighted Hankel operator H(...) and describe the conditions on which the weighted Hankel operator H(...) and weighted Toeplitz operator T(...), with (…) on the space H(...) being a sequence of positive numbers with (…), commute. It is also proved that if a non-zero weighted Hankel operator H(...) commutes with T(...), which is not a multiple of the identity, then H(...), for some (…).

3

The geometry of Darlington synthesis

Dewilde P.

International Journal of Applied Mathematics and Computer Science

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2001

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Vol. 11, no 6

1379-1386

EN

We revisit the classical problem of `Darlington synthesis', or Darlington embedding. Although traditionally it is solved using analytic means, a more natural way to approach it is to use the geometric properties of a well-chosen Hankel map. The method yields surprising results. In the first place, it allows us to formulate necessary and sufficient conditions for the existence of the embedding in terms of systems properties of the transfer operation to be embedded. In addition, the approach allows us to extend the solution to situations where no analytical transform is available. The paper has a high review content, as all the results presented have been obtained during the last twenty years and have been published. However, we make a systematic attempt at formulating them in a geometric way, independent of an accidental parametrization. The benefit is clarity and generality.

4

Iterative Model Reduction of Large State-Space Systems

Huhtanen M.

International Journal of Applied Mathematics and Computer Science

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1999

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Vol. 9, no 2

245-263

EN

There exist criteria for reducing the order of a large state-space model based on the accuracy of the approximate solutions to the Lyapunov matrix equations and the Hankel operator. Iterative solution techniques for the Lyapunov equations with the Arnoldi method have been proposed in a number of papers. In this paper we derive error bounds for approximations to the solutions to the Lyapunov equations as well as for the Hankel operator that indicate how to precondition while solving these equations iteratively.These bounds show that the error depends on three terms: First, on the amount of invariance of the constructed subspace for A, second, on the eigenvalues of A at least in proportion to 1/|Re l|, and third, under a certain condition on projectors P_l=W_lW_l* ,on the factor min_{X in C^{l x p}}|| B-( l I-A)W_lX|| for l on a path G surrounding the spectrum of A. Consequently, in order to compensate for those parts of the spectrum where 1/|Re l| is not small, preconditioning or an inverse iteration is needed to keep the sizes of the matrices used in construction of a reduced-order model moderate.