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An Approach to Solving Input Reconstruction Problems in Stochastic Differential Equations: Dynamic Algorithms and Tuning Their Parameters

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Mathematical Optimization Theory and Operations Research (MOTOR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13930))

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Abstract

Within the framework of the key approach from the theory of dynamic inversion, input reconstruction problems for stochastic differential equations are investigated. Different types of input information are used for the simultaneous reconstruction of disturbances in both the deterministic and stochastic terms of the equations. Feasible solving algorithms are designed; estimates of their convergence rates are derived. An empirical procedure adapting an algorithm to a specific system’s dynamics to obtain best approximation results is discussed. An illustrative example for this technique is presented.

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Acknowledgements

The work was performed as part of research conducted in the Ural Mathematical Center with the financial support of the Ministry of Science and Higher Education of the Russian Federation (Agreement number 075-02-2023-913).

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Authors and Affiliations

  1. Krasovskii Institute of Mathematics and Mechanics UB RAS, S. Kovalevskoi str. 16, Yekaterinburg, 620108, Russia

    Valeriy Rozenberg

  2. Ural Federal University, Mira str. 19, Yekaterinburg, 620002, Russia

    Valeriy Rozenberg

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  1. Valeriy Rozenberg
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Correspondence to Valeriy Rozenberg .

Editor information

Editors and Affiliations

  1. Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Michael Khachay

  2. Sobolev Institute of Mathematics, Novosibirsk, Russia

    Yury Kochetov

  3. Sobolev Institute of Mathematics, Omsk, Russia

    Anton Eremeev

  4. Melentiev Energy Systems Institute, Irkutsk, Russia

    Oleg Khamisov

  5. Institute of Applied Mathematical Research, Petrozavodsk, Russia

    Vladimir Mazalov

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

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Rozenberg, V. (2023). An Approach to Solving Input Reconstruction Problems in Stochastic Differential Equations: Dynamic Algorithms and Tuning Their Parameters. In: Khachay, M., Kochetov, Y., Eremeev, A., Khamisov, O., Mazalov, V., Pardalos, P. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2023. Lecture Notes in Computer Science, vol 13930. Springer, Cham. https://doi.org/10.1007/978-3-031-35305-5_27

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  • DOI: https://doi.org/10.1007/978-3-031-35305-5_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35304-8

  • Online ISBN: 978-3-031-35305-5

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