Optimization of Approximate Maps for Linear Systems Arising in Discretized PDEs

TL;DR

This paper investigates how to optimize sparse approximate maps (SAMs) to efficiently update preconditioners for sequences of linear systems from discretized PDEs, improving iterative solver convergence.

Contribution

It characterizes optimal and near-optimal sparsity patterns for SAM updates in linear systems from PDE discretizations, enhancing preconditioning efficiency.

Findings

Identifies effective sparsity patterns for SAM updates.

Demonstrates improved convergence with optimized patterns.

Provides guidelines for preconditioner updates in PDE discretizations.

Abstract

Generally, discretization of partial differential equations (PDEs) creates a sequence of linear systems $A_k x_k = b_k, k = 0, 1, 2, ..., N$ with well-known and structured sparsity patterns. Preconditioners are often necessary to achieve fast convergence When solving these linear systems using iterative solvers. We can use preconditioner updates for closely related systems instead of computing a preconditioner for each system from scratch. One such preconditioner update is the sparse approximate map (SAM), which is based on the sparse approximate inverse preconditioner using a least squares approximation. A SAM then acts as a map from one matrix in the sequence to another nearby one for which we have an effective preconditioner. To efficiently compute an effective SAM update (i.e., one that facilitates fast convergence of the iterative solver), we seek to compute an optimal sparsity pattern. In this paper, we examine several sparsity patterns for computing the SAM update to characterize optimal or near-optimal sparsity patterns for linear systems arising from discretized PDEs.