We report on a comparative study of the alternatives to reduce the complexity of the firstorder regular perturbation (FRP) model enhanced via a gradient-based optimization. We show that magnitude-based pruning of the optimized coefficients leads to a 91% complexity reduction compared to conventional FRP.
