On the Asymptotic Linear Convergence Speed of Anderson Acceleration Applied to ADMM

Empirical results show that Anderson acceleration (AA) can be a powerful mechanism to improve the asymptotic linear convergence speed of the Alternating Direction Method of Multipliers (ADMM) when ADMM by itself converges linearly. However, theoretical results to quantify this improvement do not exist yet. In this paper we explain and quantify this improvement in linear asymptotic convergence speed for the special case of a stationary version of AA applied to ADMM. We do so by considering the spectral properties of the Jacobians of ADMM and the stationary version of AA evaluated at the fixed point, where the coefficients of the stationary AA method are computed such that its asymptotic linear convergence factor is optimal. The optimal linear convergence factors of this stationary AA-ADMM method are computed analytically or by optimization, based on previous work on optimal stationary AA acceleration. Using this spectral picture and those analytical results, our approach provides new insight into how and by how much the stationary AA method can improve the asymptotic linear convergence factor of ADMM. Numerical results also indicate that the optimal linear convergence factor of the stationary AA methods gives a useful estimate for the asymptotic linear convergence speed of the non-stationary AA method that is used in practice.

Automated summary

Empirical results demonstrate that Anderson acceleration can enhance the asymptotic linear convergence speed of the Alternating Direction Method of Multipliers (ADMM) when ADMM converges linearly. The paper explains and quantifies this improvement for a special case of a stationary version of Anderson acceleration applied to ADMM. Numerical results suggest that the optimal linear convergence factor of stationary AA method can give a useful estimate for the asymptotic linear convergence speed of the non-stationary AA method used in practice.