An Augmented Lagrangian Filter Method for RealTime Embedded Optimization
Abstract
We present a filter linesearch 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 realtime 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 realtime 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 WisconsinMadison, Madison, WI (United States)
 Publication Date:
 Research Org.:
 Univ. of WisconsinMadison, 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 00189286
 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 RealTime 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 RealTime Embedded Optimization. United States. doi:10.1109/TAC.2017.2694806.
Chiang, Nai Yuan, Huang, Rui, and Zavala, Victor M. Mon .
"An Augmented Lagrangian Filter Method for RealTime Embedded Optimization". United States. doi:10.1109/TAC.2017.2694806. https://www.osti.gov/servlets/purl/1434487.
@article{osti_1434487,
title = {An Augmented Lagrangian Filter Method for RealTime Embedded Optimization},
author = {Chiang, Nai Yuan and Huang, Rui and Zavala, Victor M.},
abstractNote = {We present a filter linesearch 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 realtime 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 realtime 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 = {2017},
month = {4}
}
Web of Science