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Persistent URL http://purl.org/net/epubs/work/30887752
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Record Id 30887752
Title Solving mixed sparse-dense linear least squares by preconditioned iterative methods
Contributors
Abstract The efficient solution of large linear least squares problems in which the system matrix A contains rows with very different densities is challenging. Previous work has focused on direct methods for problems in which A has a few rows that have a relatively large number of entries. These dense rows are initially ignored, a factorization of the sparse part is computed using a sparse direct solver and then the solution updated to take account of the omitted dense rows. In some practical applications the number of dense rows can be significant. In this paper, we propose processing such rows separately within a conjugate gradient method using an incomplete factorization preconditioner and the factorization of a dense matrix of size equal to the number of rows identified as dense. Numerical experiments on large-scale problems from real applications are used to illustrate the effectiveness of our approach.
Organisation STFC , SCI-COMP , SCI-COMP-CM
Keywords incomplete factorizations , least squares problems , preconditioning , dense rows , conjugate gradients , sparse matrices
Funding Information
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Language English (EN)
Type Details URI(s) Local file(s) Year
Preprint RAL Preprints RAL-P-2017-001, SIAM J Sci Comput STFC, 2017. RAL-P-2017-001.pdf 2017
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