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Mathematics
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Mathematics
Article . 2022
Data sources: DOAJ
https://dx.doi.org/10.48550/ar...
Article . 2022
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Reconstruction of Higher-Order Differential Operators by Their Spectral Data

Authors: Natalia P. Bondarenko;

Reconstruction of Higher-Order Differential Operators by Their Spectral Data

Abstract

This paper is concerned with inverse spectral problems for higher-order (n>2) ordinary differential operators. We develop an approach to the reconstruction from the spectral data for a wide range of differential operators with either regular or distribution coefficients. Our approach is based on the reduction of an inverse problem to a linear equation in the Banach space of bounded infinite sequences. This equation is derived in a general form that can be applied to various classes of differential operators. The unique solvability of the linear main equation is also proved. By using the solution of the main equation, we derive reconstruction formulas for the differential expression coefficients in the form of series and prove the convergence of these series for several classes of operators. The results of this paper can be used for the constructive solution of inverse spectral problems and for the investigation of their solvability and stability.

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Keywords

constructive solution, method of spectral mappings, 34A55 34B09 34B05 34E05 46F10, higher-order differential operators, Mathematics - Spectral Theory, QA1-939, FOS: Mathematics, inverse spectral problems, inverse spectral problems; higher-order differential operators; distribution coefficients; constructive solution; method of spectral mappings, distribution coefficients, Spectral Theory (math.SP), Mathematics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Top 10%
Average
Top 10%
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gold