An iterative method with variable relaxation parameters for saddle-point problems

In this paper, we propose an inexact Uzawa method with variable relaxation parameters for iteratively solving linear saddle-point problems. The method involves two variable relaxation parameters, which can be updated easily in each iteration, similar to the evaluation of the two iteration parameters in the conjugate gradient method. This new algorithm has an advantage over most existing Uzawa-type algorithms: it is always convergent without any a priori estimates on the spectrum of the preconditioned Schur complement matrix, which may not be easy to achieve in applications. The rate of the convergence of the inexact Uzawa method is analyzed. Numerical results of the algorithm applied for the Stokes problem and a purely linear system of algebraic equations are presented.