WLS Design of Arma Graph Filters Using Iterative Second-Order Cone Programming

Abstract

We propose a weighted least-square (WLS) method to design autoregressive moving average (ARMA) graph filters. We first express the WLS design problem as a numerically-stable optimization problem using Chebyshev polynomial bases. We then formulate the optimization problem with a non-convex objective function and linear constraints for stability. We employ a relaxation technique and convert the non-convex optimization problem into an iterative second-order cone programming problem. Experimental results confirm that ARMA graph filters designed using the proposed WLS method have significantly improved frequency responses compared to those designed using previously proposed WLS design methods.