A Low-Complexity $L_{\infty }$ -Norm Adaptive Filtering Algorithm

We present a low-complexity minimum L_infin-norm adaptive filtering algorithm with sparse updates. A new constrained minimization problem based on the minimum disturbance in the L_infin-norm sense is developed. Solving this minimization problem gives birth to an efficient algorithm which decreases the number of updates as well as the complexity per each iteration. Experimental results comparing the proposed algorithm to the conventional algorithms clearly indicate its good convergence performance with greatly reduced complexity.