Jan 2, 2017 · In recent years, a variety of preconditioners have been proposed for use in solving large sparse linear least-squares problems.
Least squares are used across a wide range of disciplines: everything from simple curve fitting, through the estimation of.
The state-of-the-art of preconditioners for sparse linear least squares problems. Nick Gould and Jennifer Scott. STFC Rutherford Appleton Laboratory. SIAM ...
... problems”, Preprint. RAL-P-2015-010, revision 1), in which preconditioned iterative and direct methods are used to solve sparse linear least-squares problems.
May 23, 2017 · In this study, we briefly review preconditioners for which software has been made available and then present a numerical evaluation of them ...
LA15 - MS43-1 The State-of-the-Art of Preconditioners for Sparse Linear Least Squares Problems (PDF). View PDF Handout. You finished this item!
Duration: 26:08
Posted: Nov 4, 2015
Posted: Nov 4, 2015
In this paper we present a method for computing sparse preconditioners for iteratively solving rank deficient least squares problems (LS) by the LSMR method.
Jan 31, 2018 · Gould, N.I.M., Scott, J.A.: The state-of-the-art of preconditioners for sparse linear least squares problems. ACM Trans. Math. Softw. 43(4) ...
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This paper describes a technique for constructing robust preconditioners for the CGLS method applied to the solution of large and sparse least squares problems.