Accelerated Residual Methods for the Iterative Solution of Systems of Equations

Abstract

We present accelerated residual methods for the iterative solution of systems of equations by leveraging recent developments in accelerated gradient methods for convex optimization. The stability properties of the proposed method are analyzed for linear systems of equations by using the finite difference equation theory. Next, we introduce a residual descent restarting strategy and an adaptive computation of the acceleration parameter to enhance the robustness and efficiency of our method. Furthermore, we incorporate preconditioning techniques into the proposed method to accelerate its convergence. We demonstrate the performance of our method on systems of equations resulting from the finite element approximation of linear and nonlinear partial differential equations. In a variety of test cases, the numerical results show that the proposed method is competitive with the pseudo–time-marching method, Nesterov’s method, and Newton–Krylov methods. Finally, we discuss some open issues that should be addressed in future research.