Abstract
In this paper we study numerical behavior of several iterative Krylov subspace solvers applied to the solution of large-scale saddle point problems. Two main representatives of segregated solution approach are analyzed: the Schur complement reduction method based on the elimination of primary unknowns and the null-space projection method, which relies on a basis for the subspace described by the constraints. We show that the choice of the back-substitution formula may considerably influence the maximum attainable accuracy of approximate solutions computed in finite precision arithmetic.
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