Abstract
We develop a customized method of multipliers algorithm to efficiently solve a class of regularized optimal control problems. By exploiting the problem structure, we transform the augmented Lagrangian into a form which can be efficiently minimized using proximal methods. We apply our algorithm to an ℓ1-regularized state-feedback optimal control problem and compare its performance with a proximal gradient algorithm and an alternating direction method of multipliers algorithm. In contrast to other methods, our algorithm has both a theoretical guarantee of convergence and fast computation speed in practice.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 American Control Conference, ACC 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1942-1947 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781467386821 |
| DOIs | |
| State | Published - Jul 28 2016 |
| Event | 2016 American Control Conference, ACC 2016 - Boston, United States Duration: Jul 6 2016 → Jul 8 2016 |
Publication series
| Name | Proceedings of the American Control Conference |
|---|---|
| Volume | 2016-July |
| ISSN (Print) | 0743-1619 |
Other
| Other | 2016 American Control Conference, ACC 2016 |
|---|---|
| Country/Territory | United States |
| City | Boston |
| Period | 7/6/16 → 7/8/16 |
Bibliographical note
Publisher Copyright:© 2016 American Automatic Control Council (AACC).
Keywords
- Augmented Lagrangian
- Method of multipliers
- Non-smooth optimization
- Proximal methods
- Sparsity-promoting optimal control
- Structure identification