Optimization in the Complex Domain for Nonlinear Adaptive Filtering

Abstract

We present a framework that greatly simplifies the evaluations and analyses for optimization in the complex plane through the use of a generalized definition of analyticity. We derive the gradient, the relative (natural) gradient, Newton, and Newton variation updates by using this result and demonstrate its application in system identification using linear and multi-layer perceptron filters.