A fast and memory-efficient spectral Galerkin scheme for distributed elliptic optimal control problems

TL;DR

This paper presents a high-order spectral Galerkin method with a specialized preconditioner for efficiently solving large-scale distributed elliptic optimal control problems governed by convection-diffusion-reaction equations, ensuring fast convergence and low memory usage.

Contribution

It introduces a novel spectral Galerkin scheme combined with a matrix-free preconditioner tailored for CDR-based optimal control problems, improving computational efficiency.

Findings

Krylov methods converge in few iterations regardless of problem size

Preconditioner operates with linear complexity

Numerical results confirm robustness and efficiency

Abstract

Many scientific and engineering challenges can be formulated as optimization problems which are constrained by partial differential equations (PDEs). These include inverse problems, control problems, and design problems. As a major challenge, the associated optimization procedures are inherently large-scale. To ensure computational tractability, the design of efficient and robust iterative methods becomes imperative. To meet this challenge, this paper introduces a fast and memory-efficient preconditioned iterative scheme for a class of distributed optimal control problems governed by convection-diffusion-reaction (CDR) equations. As an alternative to low-order discretizations and Schur-complement block preconditioners, the scheme combines a high-order spectral Galerkin method with an efficient preconditioner tailored specifically for the CDR application. The preconditioner is matrix-free and can be applied within linear complexity where the proportionality constant is small. Numerical results demonstrate that the preconditioner is ideal in the sense that appropriate Krylov subspace methods converge within a low number of iterations, independently of the problem size and the Tikhonov regularization parameter.