A Robust Preconditioner with Low Memory Requirements for Large Sparse Least Squares Problems
A Robust Preconditioner with Low Memory Requirements for Large Sparse Least Squares Problems
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DOI：
10.1137/s106482750240649x
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发表时间：
2003-02
期刊：
影响因子：
--
通讯作者：
M. Benzi;M. Tuma
中科院分区：
文献类型：
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作者：
M. Benzi;M. Tuma
This paper describes a technique for constructing robust preconditioners for the CGLS method applied to the solution of large and sparse least squares problems. The algorithm computes an incomplete LDLT factorization of the normal equations matrix without the need to form the normal matrix itself. The preconditioner is reliable (pivot breakdowns cannot occur) and has low intermediate storage requirements. Numerical experiments illustrating the performance of the preconditioner are presented. A comparison with incomplete QR preconditioners is also included.
