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Sparse linear least-squares problems
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DOI：10.1017/S0962492924000059.png)
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Abstract
En 中文
Least-squares problems are a cornerstone of computational science and engineering. Over the years, the size of the problems that researchers and practitioners face has constantly increased, making it essential that sparsity is exploited in the solution process. The goal of this article is to present a broad review of key algorithms for solving large-scale linear least-squares problems. This includes sparse direct methods and algebraic preconditioners that are used in combination with iterative solvers. Where software is available, this is highlighted.
Keywords:
ROBUST INCOMPLETE FACTORIZATION
PSEUDO-SKELETON APPROXIMATIONS
MULTIFRONTAL QR FACTORIZATION
ITERATIVE REFINEMENT
CHOLESKY FACTORIZATION
ORTHOGONAL FACTORIZATION
DOMAIN DECOMPOSITION
NUMERICAL-SOLUTION
NESTED DISSECTION
CROUT VERSIONS
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