Sparse Matrix Factorizations for Fast Linear Solvers with Application to Laplacian Systems

Journal article

In solving a linear system with iterative methods, one is usually confronted with the dilemma of having to choose between cheap, ineffcient iterates over sparse search directions (e.g., coordinate descent), or expensive iterates in well-chosen search directions (e.g., conjugate gradients). In this paper, we propose to interpolate between these two extremes, and show how to perform cheap iterations along nonsparse search directions, provided that these directions can be extracted from a new kind of sparse factorization. For example, if the search directions are the columns of a hierarchical matrix, then the cost of each iteration is typically logarithmic in the number of variables. Using some graph-Theoretical results on low-stretch spanning trees, we deduce as a special case a nearly linear time algorithm to approximate the minimal norm solution of a linear system Bx = b where B is the incidence matrix of a graph. We thereby can connect our results to recently proposed nearly linear time solvers for Laplacian systems, which emerge here as a particular application of our sparse matrix factorization.

Authors: Schaub, M; Trefois, M; van Dooren, P; Delvenne, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Society for Industrial and Applied Mathematics
• Journal: SIAM Journal on Matrix Analysis and Applications
• Volume: 38
• Issue: 2
• Pages: 505-529
• Publication date: 2017-02-13
• Acceptance date: 2017-06-15
• DOI: 10.1137/16M1077398
• EISSN: 1095-7162
• ISSN: 0895-4798
• Keywords: Laplacian matrix; matrix factorization; iterative algorithms; sparsity; linear system; hierarchical matrices