Locally optimum adaptive signal processing algorithms

Research output: Contribution to journalArticlepeer-review


We propose a new method for comparing constant gain adaptive signal processing algorithms. Specifically, estimates of the convergence speed of the algorithms allow for the definition of a local measure of performance (the Efficacy) that can be theoretically evaluated. By definition, the Efficacy is consistent with the fair comparison techniques introduced lately in the literature and, thus, can be used as a theoretical alternative to these methods. Using the Efficacy as a performance measure, we prove that the Newton-LMS algorithm is optimum and is thus the fastest algorithm in a very rich algorithmic class. We also prove that the regular LMS is better than any of its variants that apply a nonlinear transformation on the elements of its regression vector (such as signed régresser, quantized régresser, etc.) for an important class of input signals. Simulations support all our theoretical conclusions.

Original languageEnglish (US)
Pages (from-to)1901
Number of pages1
JournalIEEE Transactions on Signal Processing
Issue number7
StatePublished - 1997
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Locally optimum adaptive signal processing algorithms'. Together they form a unique fingerprint.

Cite this