An Augmented Lagrangian Filter Method for Real-Time Embedded Optimization
Abstract
We present a filter line-search algorithm for nonconvex continuous optimization that combines an augmented Lagrangian function and a constraint violation metric to accept and reject steps. The approach is motivated by real-time optimization applications that need to be executed on embedded computing platforms with limited memory and processor speeds. The proposed method enables primal–dual regularization of the linear algebra system that in turn permits the use of solution strategies with lower computing overheads. We prove that the proposed algorithm is globally convergent and we demonstrate the developments using a nonconvex real-time optimization application for a building heating, ventilation, and air conditioning system. Our numerical tests are performed on a standard processor and on an embedded platform. Lastly, we demonstrate that the approach reduces solution times by a factor of over 1000.
- Authors:
-
- United Technologies Center, East Hartford, CT (United States)
- Univ. of Wisconsin-Madison, Madison, WI (United States)
- Publication Date:
- Research Org.:
- Univ. of Wisconsin, Madison, WI (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1434487
- Grant/Contract Number:
- SC0014114
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Automatic Control
- Additional Journal Information:
- Journal Volume: 62; Journal Issue: 12; Journal ID: ISSN 0018-9286
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; control; embedded; nonconvex; optimization; real time
Citation Formats
Chiang, Nai -Yuan, Huang, Rui, and Zavala, Victor M. An Augmented Lagrangian Filter Method for Real-Time Embedded Optimization. United States: N. p., 2017.
Web. doi:10.1109/TAC.2017.2694806.
Chiang, Nai -Yuan, Huang, Rui, & Zavala, Victor M. An Augmented Lagrangian Filter Method for Real-Time Embedded Optimization. United States. https://doi.org/10.1109/TAC.2017.2694806
Chiang, Nai -Yuan, Huang, Rui, and Zavala, Victor M. Mon .
"An Augmented Lagrangian Filter Method for Real-Time Embedded Optimization". United States. https://doi.org/10.1109/TAC.2017.2694806. https://www.osti.gov/servlets/purl/1434487.
@article{osti_1434487,
title = {An Augmented Lagrangian Filter Method for Real-Time Embedded Optimization},
author = {Chiang, Nai -Yuan and Huang, Rui and Zavala, Victor M.},
abstractNote = {We present a filter line-search algorithm for nonconvex continuous optimization that combines an augmented Lagrangian function and a constraint violation metric to accept and reject steps. The approach is motivated by real-time optimization applications that need to be executed on embedded computing platforms with limited memory and processor speeds. The proposed method enables primal–dual regularization of the linear algebra system that in turn permits the use of solution strategies with lower computing overheads. We prove that the proposed algorithm is globally convergent and we demonstrate the developments using a nonconvex real-time optimization application for a building heating, ventilation, and air conditioning system. Our numerical tests are performed on a standard processor and on an embedded platform. Lastly, we demonstrate that the approach reduces solution times by a factor of over 1000.},
doi = {10.1109/TAC.2017.2694806},
journal = {IEEE Transactions on Automatic Control},
number = 12,
volume = 62,
place = {United States},
year = {Mon Apr 17 00:00:00 EDT 2017},
month = {Mon Apr 17 00:00:00 EDT 2017}
}
Web of Science
Works referencing / citing this record:
Robust control for nonlinear systems with uncertain time-varying parameters coupled with non-triangular terms
journal, January 2020
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