Fast Adaptive First-Order Semidefinite Programming for Data-Driven Linear Quadratic Regulation

Reza Rahimi Baghbadorani; Peyman Mohajerin Esfahani; Sergio Grammatico

doi:10.1109/MED61351.2024.10566178

Fast Adaptive First-Order Semidefinite Programming for Data-Driven Linear Quadratic Regulation

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

We study the data-driven finite-horizon linear quadratic regularization (LQR) problem reformulated as a semidefinite program (SDP). Our contribution is to propose two novel accelerated first-order methods for solving the resulting SDP. Our methods enjoy adaptive stepsize and adaptive smoothing parameters that speed up convergence and in turn, enhance scalability. Finally, we compare our accelerated first-order methods and show their benefits via numerical simulations on a benchmark LQR example.

Original language	English
Title of host publication	Proceedings of the 32nd Mediterranean Conference on Control and Automation, MED 2024
Publisher	IEEE
Pages	771-776
Number of pages	6
ISBN (Electronic)	979-8-3503-9544-0
DOIs	https://doi.org/10.1109/MED61351.2024.10566178
Publication status	Published - 2024
Event	32nd Mediterranean Conference on Control and Automation, MED 2024 - Chania, Crete, Greece Duration: 11 Jun 2024 → 14 Jun 2024

Conference

Conference	32nd Mediterranean Conference on Control and Automation, MED 2024
Country/Territory	Greece
City	Chania, Crete
Period	11/06/24 → 14/06/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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Fast_Adaptive_First-Order_Semidefinite_Programming_for_Data-Driven_Linear_Quadratic_RegulationFinal published version, 558 KB

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@inproceedings{1f6bc5ff8ff24d459967e2c5f6a4da91,

title = "Fast Adaptive First-Order Semidefinite Programming for Data-Driven Linear Quadratic Regulation",

abstract = "We study the data-driven finite-horizon linear quadratic regularization (LQR) problem reformulated as a semidefinite program (SDP). Our contribution is to propose two novel accelerated first-order methods for solving the resulting SDP. Our methods enjoy adaptive stepsize and adaptive smoothing parameters that speed up convergence and in turn, enhance scalability. Finally, we compare our accelerated first-order methods and show their benefits via numerical simulations on a benchmark LQR example.",

author = "Baghbadorani, \{Reza Rahimi\} and Esfahani, \{Peyman Mohajerin\} and Sergio Grammatico",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 32nd Mediterranean Conference on Control and Automation, MED 2024 ; Conference date: 11-06-2024 Through 14-06-2024",

year = "2024",

doi = "10.1109/MED61351.2024.10566178",

language = "English",

pages = "771--776",

booktitle = "Proceedings of the 32nd Mediterranean Conference on Control and Automation, MED 2024",

publisher = "IEEE",

address = "United States",

}

Baghbadorani, RR , Esfahani, PM & Grammatico, S 2024, Fast Adaptive First-Order Semidefinite Programming for Data-Driven Linear Quadratic Regulation. in Proceedings of the 32nd Mediterranean Conference on Control and Automation, MED 2024. IEEE, pp. 771-776, 32nd Mediterranean Conference on Control and Automation, MED 2024, Chania, Crete, Greece, 11/06/24. https://doi.org/10.1109/MED61351.2024.10566178

Fast Adaptive First-Order Semidefinite Programming for Data-Driven Linear Quadratic Regulation. / Baghbadorani, Reza Rahimi ; Esfahani, Peyman Mohajerin ; Grammatico, Sergio.
Proceedings of the 32nd Mediterranean Conference on Control and Automation, MED 2024. IEEE, 2024. p. 771-776.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2024

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N2 - We study the data-driven finite-horizon linear quadratic regularization (LQR) problem reformulated as a semidefinite program (SDP). Our contribution is to propose two novel accelerated first-order methods for solving the resulting SDP. Our methods enjoy adaptive stepsize and adaptive smoothing parameters that speed up convergence and in turn, enhance scalability. Finally, we compare our accelerated first-order methods and show their benefits via numerical simulations on a benchmark LQR example.

AB - We study the data-driven finite-horizon linear quadratic regularization (LQR) problem reformulated as a semidefinite program (SDP). Our contribution is to propose two novel accelerated first-order methods for solving the resulting SDP. Our methods enjoy adaptive stepsize and adaptive smoothing parameters that speed up convergence and in turn, enhance scalability. Finally, we compare our accelerated first-order methods and show their benefits via numerical simulations on a benchmark LQR example.

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DO - 10.1109/MED61351.2024.10566178

M3 - Conference contribution

AN - SCOPUS:85198224422

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BT - Proceedings of the 32nd Mediterranean Conference on Control and Automation, MED 2024

PB - IEEE

T2 - 32nd Mediterranean Conference on Control and Automation, MED 2024

Y2 - 11 June 2024 through 14 June 2024

ER -

Fast Adaptive First-Order Semidefinite Programming for Data-Driven Linear Quadratic Regulation

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