Published In

ACM Transactions on Mathematical Software

Document Type

Article

Publication Date

2018

Subjects

Software architecture -- Development

Abstract

This article has two main objectives: one is to describe some extensions of an adaptive Algebraic Multigrid (AMG) method of the form previously proposed by the first and third authors, and a second one is to present a new software framework, named BootCMatch, which implements all the components needed to build and apply the described adaptive AMG both as a stand-alone solver and as a preconditioner in a Krylov method. The adaptive AMG presented is meant to handle general symmetric and positive definite (SPD) sparse linear systems, without assuming any a priori information of the problem and its origin; the goal of adaptivity is to achieve a method with a prescribed convergence rate. The presented method exploits a general coarsening process based on aggregation of unknowns, obtained by a maximum weight matching in the adjacency graph of the system matrix. More specifically, a maximum product matching is employed to define an effective smoother subspace (complementary to the coarse space), a process referred to as compatible relaxation, at every level of the recursive two-level hierarchical AMG process.

Description

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Locate the Document

https://10.1145/3190647

DOI

10.1145/3190647

Persistent Identifier

https://archives.pdx.edu/ds/psu/26698

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