LMI Approximations for the Radius of the Intersection of Ellipsoids: Survey

D. Henrion, S. Tarbouriech and D. Arzelier
Additional contact information
D. Henrion: Centre National de la Recherche Scientifique
S. Tarbouriech: Centre National de la Recherche Scientifique
D. Arzelier: Centre National de la Recherche Scientifique

Journal of Optimization Theory and Applications, 2001, vol. 108, issue 1, No 1, 28 pages

Abstract: Abstract This paper surveys various linear matrix inequality relaxation techniques for evaluating the maximum norm vector within the intersection of several ellipsoids. This difficult nonconvex optimization problem arises frequently in robust control synthesis. Two randomized algorithms and several ellipsoidal approximations are described. Guaranteed approximation bounds are derived in order to evaluate the quality of these relaxations.

Keywords: linear matrix inequalities; nonconvex optimization; convex relaxations; ellipsoids; robust control (search for similar items in EconPapers)
Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1023/A:1026454804250 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:108:y:2001:i:1:d:10.1023_a:1026454804250

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1023/A:1026454804250

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().