Minimum-norm realization of 2D recursive filters: a quasiconvex programming approach

Author

Lu, W.-S.

Author_Institution

Dept. of Electr. & Comput. Eng., Victoria Univ., BC, Canada

Volume

3

fYear

1999

fDate

36342

Firstpage

335

Abstract

The minimum-norm realization (MNR) of a 2D recursive digital filter is considered and it is shown that an MNR problem can be formulated as a quasiconvex optimization problem in which the largest generalized eigenvalue of a certain matrix pencil is minimized. It is demonstrated that efficient interior-point convex programming techniques such as Nesterov and Nemirovskii´s projective method can be used to perform the optimization with considerably reduced computational complexity compared to the existing minimization techniques

Keywords

computational complexity; convex programming; eigenvalues and eigenfunctions; recursive filters; two-dimensional digital filters; 2D recursive filters; computational complexity; generalized eigenvalue; interior-point convex programming techniques; matrix pencil; minimum-norm realization; projective method; quasiconvex programming approach; Computational complexity; Control systems; Digital filters; Eigenvalues and eigenfunctions; Limit-cycles; Minimization methods; Nonlinear filters; Optimization methods; Quadratic programming; Symmetric matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-5471-0

Type

conf

DOI

10.1109/ISCAS.1999.778853

Filename

778853