On Generalized Preconditioners for Time-Parallel Parabolic Optimal Control

Abstract

SIAM Journal on Scientific Computing, Volume 46, Issue 4, Page A2298-A2323, August 2024.
Abstract. The ParaDiag family of algorithms solves differential equations by using preconditioners that can be inverted in parallel through diagonalization. In the context of optimal control of linear parabolic PDEs, the state-of-the-art ParaDiag method is limited to solving self-adjoint problems with a tracking objective. We propose three improvements to the ParaDiag method: the use of alpha-circulant matrices to construct an alternative preconditioner, a generalization of the algorithm for solving non-self-adjoint equations, and the formulation of an algorithm for terminal-cost objectives. We present novel analytic results about the eigenvalues of the preconditioned systems for all discussed ParaDiag algorithms in the case of self-adjoint equations, which proves the favorable properties of the alpha-circulant preconditioner. We use these results to perform a theoretical parallel-scaling analysis of ParaDiag for self-adjoint problems. Numerical tests confirm our findings and suggest that the self-adjoint behavior, which is backed by theory, generalizes to the non-self-adjoint case. We provide a sequential, open-source reference solver in MATLAB for all discussed algorithms. Reproducibility of computational results. This paper has been awarded the “SIAM Reproducibility Badge: Code and data available,” as a recognition that the authors have followed reproducibility principles valued by SISC and the scientific computing community. Code and data that allow readers to reproduce the results in this paper are available at https://gitlab.kuleuven.be/numa/public/pintopt or in the supplementary materials (repro-generalized-paradiag.zip [86.4KB]).