Academic Journal

Stochastic LQ Control with Extra Measurability Restriction.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Stochastic LQ Control with Extra Measurability Restriction.
Συγγραφείς: Wang, Hongxia, Hu, Yuxi, Li, Zixing, Song, Lianfeng
Πηγή: Journal of Systems Science & Complexity; Jun2024, Vol. 37 Issue 3, p1003-1022, 20p
Περίληψη: Different from the standard linear quadratic (LQ) problem for stochastic systems, the LQ problem considered in the paper has extra measurability restrictions. The problem also appears in the LQ control problem for stochastic systems with delays, rational expectations problems, asymmetric information control, and so on. The essential difficulty lies in that one has to optimize the input and its conditional expectations simultaneously. The stochastic maximum principle (SMP) and orthogonal decomposition technique are the key tools. Firstly, the authors establish the SMP and convert the original problem into forward and backward stochastic difference equations (FBSDEs) with extra measurability restrictions. Secondly, the authors resolve the FBSDEs by using the orthogonal decomposition technique and obtain the analytical solution to the underlying problem. Thirdly, the authors explore the essential distinction between the problem and the standard stochastic LQ control problem. Finally, numerical examples are given to illustrate the obtained results. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Systems Science & Complexity is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  – Url: https://dx.doi.org/doi:10.1007/s11424-024-2501-0
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  Data: Stochastic LQ Control with Extra Measurability Restriction.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Hongxia%22">Wang, Hongxia</searchLink><br /><searchLink fieldCode="AR" term="%22Hu%2C+Yuxi%22">Hu, Yuxi</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Zixing%22">Li, Zixing</searchLink><br /><searchLink fieldCode="AR" term="%22Song%2C+Lianfeng%22">Song, Lianfeng</searchLink>
– Name: TitleSource
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  Data: Journal of Systems Science & Complexity; Jun2024, Vol. 37 Issue 3, p1003-1022, 20p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Different from the standard linear quadratic (LQ) problem for stochastic systems, the LQ problem considered in the paper has extra measurability restrictions. The problem also appears in the LQ control problem for stochastic systems with delays, rational expectations problems, asymmetric information control, and so on. The essential difficulty lies in that one has to optimize the input and its conditional expectations simultaneously. The stochastic maximum principle (SMP) and orthogonal decomposition technique are the key tools. Firstly, the authors establish the SMP and convert the original problem into forward and backward stochastic difference equations (FBSDEs) with extra measurability restrictions. Secondly, the authors resolve the FBSDEs by using the orthogonal decomposition technique and obtain the analytical solution to the underlying problem. Thirdly, the authors explore the essential distinction between the problem and the standard stochastic LQ control problem. Finally, numerical examples are given to illustrate the obtained results. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Systems Science & Complexity is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11424-024-2501-0
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              Text: Jun2024
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              Y: 2024
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