Global and local optimization in identification of parabolic systems Full article
Journal |
Journal of Inverse and Ill-Posed Problems
ISSN: 0928-0219 , E-ISSN: 1569-3945 |
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Output data | Year: 2020, Volume: 28, Number: 6, Pages: 899-913 Pages count : 15 DOI: 10.1515/jiip-2020-0083 | ||||||||
Tags | gradient method; Inverse problem; optimization; parameter estimation; partial differential equations; regularization; social network; tensor train; tensor train decomposition | ||||||||
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Abstract:
The problem of identification of coefficients and initial conditions for a boundary value problem for parabolic equations that reduces to a minimization problem of a misfit function is investigated. Firstly, the tensor train decomposition approach is presented as a global convergence algorithm. The idea of the proposed method is to extract the tensor structure of the optimized functional and use it for multidimensional optimization problems. Secondly, for the refinement of the unknown parameters, three local optimization approaches are implemented and compared: Nelder-Mead simplex method, gradient method of minimum errors, adaptive gradient method. For gradient methods, the evident formula for the continuous gradient of the misfit function is obtained. The identification problem for the diffusive logistic mathematical model which can be applied to social sciences (online social networks), economy (spatial Solow model) and epidemiology (coronavirus COVID-19, HIV, etc.) is considered. The numerical results for information propagation in online social network are presented and discussed. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.
Cite:
Krivorotko O.
, Kabanikhin S.
, Zhang S.
, Kashtanova V.
Global and local optimization in identification of parabolic systems
Journal of Inverse and Ill-Posed Problems. 2020. V.28. N6. P.899-913. DOI: 10.1515/jiip-2020-0083 WOS Scopus OpenAlex
Global and local optimization in identification of parabolic systems
Journal of Inverse and Ill-Posed Problems. 2020. V.28. N6. P.899-913. DOI: 10.1515/jiip-2020-0083 WOS Scopus OpenAlex
Identifiers:
Web of science: | WOS:000594396300010 |
Scopus: | 2-s2.0-85092389472 |
OpenAlex: | W3083776558 |