Numerical solution of the finite horizon stochastic linear quadratic control problem
Damm, Tobias ; Mena, Hermann and Stillfjord, Tony LU
(2017)
In Numerical Linear Algebra with Applications
24(4).
- Abstract
The treatment of the stochastic linear quadratic optimal control problem with finite time horizon requires the solution of stochastic differential Riccati equations. We propose efficient numerical methods, which exploit the particular structure and can be applied for large-scale systems. They are based on numerical methods for ordinary differential equations such as Rosenbrock methods, backward differentiation formulas, and splitting methods. The performance of our approach is tested in numerical experiments.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d58c95e8-a22a-483b-aaf8-3137e64f9591
- author
- Damm, Tobias
; Mena, Hermann
and Stillfjord, Tony
LU
- publishing date
- 2017-03-17
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- BDF methods, Rosenbrock methods, splitting methods, stochastic LQR problem, stochastic Riccati equations
- in
- Numerical Linear Algebra with Applications
- volume
- 24
- issue
- 4
- article number
- e2091
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85016432334
- ISSN
- 1070-5325
- DOI
- 10.1002/nla.2091
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: Copyright © 2017 John Wiley & Sons, Ltd.
- id
- d58c95e8-a22a-483b-aaf8-3137e64f9591
- date added to LUP
- 2024-01-23 17:30:17
- date last changed
- 2024-02-26 09:06:48
@article{d58c95e8-a22a-483b-aaf8-3137e64f9591,
abstract = {{<p>The treatment of the stochastic linear quadratic optimal control problem with finite time horizon requires the solution of stochastic differential Riccati equations. We propose efficient numerical methods, which exploit the particular structure and can be applied for large-scale systems. They are based on numerical methods for ordinary differential equations such as Rosenbrock methods, backward differentiation formulas, and splitting methods. The performance of our approach is tested in numerical experiments.</p>}},
author = {{Damm, Tobias and Mena, Hermann and Stillfjord, Tony}},
issn = {{1070-5325}},
keywords = {{BDF methods; Rosenbrock methods; splitting methods; stochastic LQR problem; stochastic Riccati equations}},
language = {{eng}},
month = {{03}},
number = {{4}},
publisher = {{Wiley-Blackwell}},
series = {{Numerical Linear Algebra with Applications}},
title = {{Numerical solution of the finite horizon stochastic linear quadratic control problem}},
url = {{http://dx.doi.org/10.1002/nla.2091}},
doi = {{10.1002/nla.2091}},
volume = {{24}},
year = {{2017}},
}