Adaptive parameter estimation using interior point optimization techniques: Convergence analysis

Kaywan H. Afkhamie, Zhi Quan Luo

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Interior Point Optimization techniques have recently emerged as a new tool for developing parameter estimation algorithms. These algorithms aim to take advantage of the fast convergence properties of interior point methods, to yield, in particular, fast transient performance. In this paper we develop a simple analytic center based algorithm, which updates estimates with a constant number of computation (independent of number of samples). The convergence analysis shows that the asymptotic performance of this algorithm matches that of the well-known least squares filter (provided some mild conditions are satisfied). Some numerical simulations are provided to demonstrate the fast transient performance of the interior point algorithm.

Original languageEnglish (US)
Pages (from-to)1681-1684
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
StatePublished - Jan 1 1999
EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
Duration: Mar 15 1999Mar 19 1999

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