Krylov Subspace Recycling for Evolving Structures

TL;DR

This paper introduces a novel method for effectively recycling Krylov subspaces in PDE constrained shape optimization problems involving evolving meshes, addressing challenges posed by mesh changes and re-meshing.

Contribution

It presents an algorithm to map invariant subspaces across changing meshes and adapts the Krylov-Schur method for improved subspace approximation during optimization.

Findings

The proposed method successfully maps invariant subspaces across mesh changes.

Numerical experiments demonstrate improved convergence in shape optimization.

The approach effectively handles structural matrix changes due to re-meshing.

Abstract

Krylov subspace recycling is a powerful tool for solving long series of large, sparse linear systems that change slowly. In PDE constrained shape optimization, these appear naturally, as hundreds or more optimization steps are needed with only small changes in the geometry. In this setting, however, applying Krylov subspace recycling can be difficult. As the geometry evolves, so does the finite element mesh, especially if re-meshing is needed. As a result, the number of algebraic degrees of freedom in the system may change from one optimization step to the next, and with it the size of the finite element system matrix. Changes in the mesh also lead to structural changes in the matrices. In the case of remeshing, even if the geometry changes only a little, the corresponding mesh might differ substantially from the previous one. This prevents any straightforward mapping of the approximate invariant subspace of the linear system matrix (the focus of recycling in this paper) from one step to the next; similar problems arise for other selected subspaces. We present an algorithm for general meshes to map an approximate invariant subspace of the system matrix for the previous optimization step to an approximate invariant subspace of the system matrix for the current optimization step. We exploit the map from coefficient vectors to finite element functions on the mesh combined with function approximation on the finite element mesh. In addition, we develop a straightforward warm-start adaptation of the Krylov-Schur algorithm [G.W. Stewart, SIAM J. Matrix Anal. Appl. 23, 2001] to improve the approximate invariant subspace at the start of a new optimization step if needed. We demonstrate the effectiveness of our approach numerically with several proof of concept studies for a specific meshing technique.