By Topic

Efficient solution of linear matrix inequalities for integral quadratic constraints

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
A. Hansson ; Dept. of Signals, Sensors & Syst., R. Inst. of Technol., Stockholm, Sweden ; L. Vandenberghe

Discusses how to implement an efficient interior-point algorithm for the semi-definite programs that result from integral quadratic constraints. The algorithm is a primal-dual potential reduction method, and the computational effort is dominated by a least-squares system that has to be solved in each iteration. The key to an efficient implementation is to utilize iterative methods and the specific structure of integral quadratic constraints. The algorithm has been implemented in Matlab. To give a rough idea of the efficiencies obtained, it is possible to solve problems resulting in a linear matrix inequality of dimension 130×130 with approximately 5000 variables in about 10 minutes on a lap-top. Problems with approximately 20000 variable and a linear matrix inequality of dimension 230×230 are solved in a few hours. It is not assumed that the system matrix has no eigenvalues on the imaginary axis, nor is it assumed that it is Hurwitz

Published in:

Decision and Control, 2000. Proceedings of the 39th IEEE Conference on  (Volume:5 )

Date of Conference: