Fast Iterative Solver for the Optimal Control of Time-Dependent PDEs with Crank-Nicolson Discretization in Time

TL;DR

This paper introduces a fast, robust iterative solver for optimal control problems constrained by time-dependent PDEs, employing Crank-Nicolson discretization and tailored preconditioning techniques for improved efficiency.

Contribution

The authors develop a novel preconditioned iterative method using Crank-Nicolson discretization, with a symmetrizing transformation and optimal Schur complement approximation for all-at-once PDE-constrained optimization.

Findings

Solver outperforms existing methods in robustness and speed

Effective across various mesh sizes and parameters

Proven optimality of Schur complement approximation

Abstract

In this article, we derive a new, fast, and robust preconditioned iterative solution strategy for the all-at-once solution of optimal control problems with time-dependent PDEs as constraints, including the heat equation and the non-steady convection--diffusion equation. After applying an optimize-then-discretize approach, one is faced with continuous first-order optimality conditions consisting of a coupled system of PDEs. As opposed to most work in preconditioning the resulting discretized systems, where a (first-order accurate) backward Euler method is used for the discretization of the time derivative, we employ a (second-order accurate) Crank--Nicolson method in time. We apply a carefully tailored invertible transformation for symmetrizing the matrix, and then derive an optimal preconditioner for the saddle-point system obtained. The key components of this preconditioner are an accurate mass matrix approximation, a good approximation of the Schur complement, and an appropriate multigrid process to apply this latter approximation---these are constructed using our work in transforming the matrix system. We prove the optimality of the approximation of the Schur complement through bounds on the eigenvalues, and test our solver against a widely-used preconditioner for the linear system arising from a backward Euler discretization. These demonstrate the effectiveness and robustness of our solver with respect to mesh-sizes, regularization parameter, and diffusion coefficient.