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Randomized algorithms for rounding in the Tensor-Train format
Hussam Al Daas, Grey Ballard, Paul Cazeaux +5
The Tensor-Train (TT) format is a highly compact low-rank representation for high-dimensional tensors. TT is particularly useful when representing approximations to the solutions o…
A Robust Algebraic Multilevel Domain Decomposition Preconditioner For Sparse Symmetric Positive Definite Matrices
Hussam Al Daas, Pierre Jolivet
Domain decomposition (DD) methods are widely used as preconditioner techniques. Their effectiveness relies on the choice of a locally constructed coarse space. Thus far, this const…
A Robust Algebraic Domain Decomposition Preconditioner for Sparse Normal Equations
Hussam Al Daas, Pierre Jolivet, Jennifer Scott
Solving the normal equations corresponding to large sparse linear least-squares problems is an important and challenging problem. For very large problems, an iterative solver is ne…
Two-level Nyström--Schur preconditioner for sparse symmetric positive definite matrices
Hussam Al Daas, Tyrone Rees, Jennifer Scott
Randomized methods are becoming increasingly popular in numerical linear algebra. However, few attempts have been made to use them in developing preconditioners. Our interest lies…