Preconditioning of Linear Least Squares by Robust Incomplete Factorization for Implicitly Held Normal Equations

Preconditioning of Linear Least Squares by Rif for Implicitly Held Normal Equations

The efficient solution of the normal equations corresponding to a large sparse linear least squares problem can be extremely challenging. Robust incomplete factorization (RIF) preconditioners represent one approach that has the important feature of computing an incomplete LLT factorization of the normal equations matrix without having to form the normal matrix itself. The right-looking implemen...

Least Squares Solutions of Inconsistent Fuzzy Linear Matrix Equations

Preconditioning of linear least-squares problems by identifying basic variables

The preconditioning of linear least-squares problems is a hard task. The linear model underpinning least-squares problems, that is the overdetermined matrix defining it, does not have the properties of differential problems that make standard preconditioners effective. Incomplete Cholesky techniques applied to the normal equations do not produce a well conditioned problem. We attempt to remove ...

Robust linear least squares regression

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least squares solutions of inconsistent fuzzy linear matrix equations

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