Smoothing analysis of two robust multigrid methods for elliptic optimal control problems

TL;DR

This paper analyzes and compares two multigrid relaxation schemes for elliptic optimal control problems, deriving optimal parameters, proposing a new scheme, and validating their efficiency through numerical experiments.

Contribution

It provides a detailed local Fourier analysis of CJR, introduces a new mass-based BSR scheme with improved smoothing factors, and extends these methods to control-constrained problems.

Findings

Optimal relaxation parameters depend on mesh and regularization parameters.

Mass-based BSR scheme outperforms CJR when lpha ch^4.

IBSR scheme with conjugate gradients improves computational efficiency.

Abstract

In this paper we study and compare two multigrid relaxation schemes with coarsening by two, three, and four for solving elliptic sparse optimal control problems with control constraints. First, we perform a detailed local Fourier analysis (LFA) of a well-known collective Jacobi relaxation (CJR) scheme, where the optimal smoothing factors are derived. This insightful analysis reveals that the optimal relaxation parameters depend on mesh size and regularization parameters, which was not investigated in literature. Second, we propose and analyze a new mass-based Braess-Sarazin relaxation (BSR) scheme, which is proven to provide smaller smoothing factors than the CJR scheme when $\alpha\ge ch^4$ for a small constant $c$. Here $\alpha$ is the regularization parameter and $h$ is the spatial mesh step size. These schemes are successfully extended to control-constrained cases through the semi-smooth Newton method. Coarsening by three or four with BSR is competitive with coarsening by two. Numerical examples are presented to validate our theoretical outcomes. The proposed inexact BSR (IBSR) scheme, where two preconditioned conjugate gradients iterations are applied to the Schur complement system, yields better computational efficiency than the CJR scheme.