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• W258377895 abstract "Publisher Summary This chapter discusses the methods for sparse linear least squares problems. It reviews direct and iterative methods for solving sparse least squares problems. These problems generally arise in the same contexts as sparse linear equations. Among the applications are geodesy, photogrammetry, statistical computations, and structural analysis. As the subject is rapidly developing and so far not enough is known about the algorithms, it has generally not been possible to make final assertions about the relative efficiency of different algorithms. Direct methods have the general advantages over iterative methods that subsequent right hand sides can be treated efficiently and it is easier to obtain elements of related inverse matrices. With direct methods, it is also possible to use the technique of iterative refinement. The most straightforward method to solve the least squares problem is to form the normal equations and then compute the Cholesky factorization. For dense problems, iterative refinement is a cheap and simple way to improve the accuracy of a computed solution. It also has the advantage that it gives useful information about the condition of the problem." @default.
• W258377895 created "2016-06-24" @default.
• W258377895 creator A5047643394 @default.
• W258377895 date "1976-01-01" @default.
• W258377895 modified "2023-09-27" @default.
• W258377895 title "METHODS FOR SPARSE LINEAR LEAST SQUARES PROBLEMS" @default.
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• W258377895 doi "https://doi.org/10.1016/b978-0-12-141050-6.50015-5" @default.
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