A Convex Approach to Frisch-Kalman Problem
Zhao, Di LU ; Rantzer, Anders LU
and Qiu, Li
(2020)
58th IEEE Conference on Decision and Control, CDC 2019
In Proceedings of the IEEE Conference on Decision and Control
2019-December.
p.7154-7158
- Abstract
This paper proposes a convex approach to the Frisch-Kalman problem that identifies the linear relations among variables from noisy observations. The problem was proposed by Ragnar Frisch in 1930s, and was promoted and further developed by Rudolf Kalman later in 1980s. It is essentially a rank minimization problem with convex constraints. Regarding this problem, analytical results and heuristic methods have been pursued over a half century. The proposed convex method in this paper is demonstrated to outperform several commonly adopted heuristics when the noise components are relatively small compared with the underlying data.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/dda265ee-cd2b-45ae-9362-e1378c56a8e4
- author
- Zhao, Di
LU
; Rantzer, Anders
LU
and Qiu, Li
- organization
- publishing date
- 2020-03-12
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2019 IEEE 58th Conference on Decision and Control, CDC 2019
- series title
- Proceedings of the IEEE Conference on Decision and Control
- volume
- 2019-December
- article number
- 9029746
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 58th IEEE Conference on Decision and Control, CDC 2019
- conference location
- Nice, France
- conference dates
- 2019-12-11 - 2019-12-13
- external identifiers
-
- scopus:85082459411
- ISSN
- 0743-1546
- 2576-2370
- ISBN
- 9781728113982
- 978-1-7281-1399-9
- DOI
- 10.1109/CDC40024.2019.9029746
- project
- Scalable Control of Interconnected Systems
- ELLIIT LU P10: Scalable Optimization for Control Systems
- WASP: Wallenberg AI, Autonomous Systems and Software Program at Lund University
- language
- English
- LU publication?
- yes
- id
- dda265ee-cd2b-45ae-9362-e1378c56a8e4
- alternative location
- https://arxiv.org/abs/1909.03932
- date added to LUP
- 2020-04-29 15:16:50
- date last changed
- 2024-05-01 10:14:17
@inproceedings{dda265ee-cd2b-45ae-9362-e1378c56a8e4,
abstract = {{<p>This paper proposes a convex approach to the Frisch-Kalman problem that identifies the linear relations among variables from noisy observations. The problem was proposed by Ragnar Frisch in 1930s, and was promoted and further developed by Rudolf Kalman later in 1980s. It is essentially a rank minimization problem with convex constraints. Regarding this problem, analytical results and heuristic methods have been pursued over a half century. The proposed convex method in this paper is demonstrated to outperform several commonly adopted heuristics when the noise components are relatively small compared with the underlying data.</p>}},
author = {{Zhao, Di and Rantzer, Anders and Qiu, Li}},
booktitle = {{2019 IEEE 58th Conference on Decision and Control, CDC 2019}},
isbn = {{9781728113982}},
issn = {{0743-1546}},
language = {{eng}},
month = {{03}},
pages = {{7154--7158}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings of the IEEE Conference on Decision and Control}},
title = {{A Convex Approach to Frisch-Kalman Problem}},
url = {{http://dx.doi.org/10.1109/CDC40024.2019.9029746}},
doi = {{10.1109/CDC40024.2019.9029746}},
volume = {{2019-December}},
year = {{2020}},
}