Rank structured approximation method for quasi--periodic elliptic problems

TL;DR

This paper introduces an efficient iterative method for solving elliptic boundary value problems with quasi-periodic structures, utilizing a preconditioner and tensor formats to handle rapidly changing coefficients with controlled complexity.

Contribution

It develops a rank-structured approximation method with explicit contraction estimates and a posteriori error control for quasi-periodic elliptic problems, improving computational efficiency.

Findings

Proves contraction of the iterative method with explicit estimates.

Establishes relations for optimal preconditioners in quasi-periodic structures.

Achieves low storage and computational complexity depending weakly on frequency parameter.

Abstract

We consider an iteration method for solving an elliptic type boundary value problem $\mathcal{A} u=f$, where a positive definite operator $\mathcal{A}$ is generated by a quasi--periodic structure with rapidly changing coefficients (typical period is characterized by a small parameter $\epsilon$) . The method is based on using a simpler operator $\mathcal{A}_0$ (inversion of $\mathcal{A}_0$ is much simpler than inversion of $\mathcal{A}$), which can be viewed as a preconditioner for $\mathcal{A}$. We prove contraction of the iteration method and establish explicit estimates of the contraction factor $q$. Certainly the value of $q$ depends on the difference between $\mathcal{A}$ and $\mathcal{A}_0$. For typical quasi--periodic structures, we establish simple relations that suggest an optimal $\mathcal{A}_0$ (in a selected set of "simple" structures) and compute the corresponding contraction factor. Further, this allows us to deduce fully computable two--sided a posteriori estimates able to control numerical solutions on any iteration. The method is especially efficient if the coefficients of $\mathcal{A}$ admit low rank representations and algebraic operations are performed in tensor structured formats. Under moderate assumptions the storage and solution complexity of our approach depends only weakly (merely linear-logarithmically) on the frequency parameter $1/\epsilon$, providing the FEM approximation of the order of $O(\epsilon^{1+p})$, $p>0$.