LINE SEARCH ALGORITHMS FOR ADAPTIVE FILTERING

Adaptive filtering algorithms which adjust their step size in order to minimize the sum of squared estimation errors on a linear manifold or ''line'' are described. The resulting algorithms have least mean squa re (LMS) complexity, zero asymptotic misadjustment for the stationary data case, and converge faster than LMS.