4 citations · 7 across the 6 of their papers we have counts for
6 papers
Efficient algorithms for computing rank-revealing factorizations on a GPU
Nathan Heavner, Chao Chen, Abinand Gopal +1
Standard rank-revealing factorizations such as the singular value decomposition and column pivoted QR factorization are challenging to implement efficiently on a GPU. A major diffi…
Efficient algorithms for computing a rank-revealing UTV factorization on parallel computing architectures
N. Heavner, F. D. Igual, G. Quintana-Ortí +1
The randomized singular value decomposition (RSVD) is by now a well established technique for efficiently computing an approximate singular value decomposition of a matrix. Buildin…
Computing rank-revealing factorizations of matrices stored out-of-core
Nathan Heavner, Per-Gunnar Martinsson, Gregorio Quintana-Ortí
This paper describes efficient algorithms for computing rank-revealing factorizations of matrices that are too large to fit in RAM, and must instead be stored on slow external memo…
Efficient nuclear norm approximation via the randomized UTV algorithm
Nathan Heavner, Per-Gunnar Martinsson
The recently introduced algorithm randUTV provides a highly efficient technique for computing accurate approximations to all the singular values of a given matrix . The original…
randUTV: A blocked randomized algorithm for computing a rank-revealing UTV factorization
Per-Gunnar Martinsson, Gregorio Quintana-Orti, Nathan Heavner
This manuscript describes the randomized algorithm randUTV for computing a so called UTV factorization efficiently. Given a matrix , the algorithm computes a factorization $A =…
Householder QR Factorization with Randomization for Column Pivoting (HQRRP). FLAME Working Note #78
Per-Gunnar Martinsson, Gregorio Quintana-Orti, Nathan Heavner +1
A fundamental problem when adding column pivoting to the Householder QR factorization is that only about half of the computation can be cast in terms of high performing matrix-matr…