Abstract
The supernodal method for sparse Cholesky factorization represents the factor
L
as a set of supernodes, each consisting of a contiguous set of columns of
L
with identical nonzero pattern. A conventional supernode is stored as a dense submatrix. While this is suitable for sparse Cholesky factorization where the nonzero pattern of
L
does not change, it is not suitable for methods that modify a sparse Cholesky factorization after a low-rank change to
A
(an update/downdate,
Ā = A ± WW
T
). Supernodes merge and split apart during an update/downdate. Dynamic supernodes are introduced which allow a sparse Cholesky update/downdate to obtain performance competitive with conventional supernodal methods. A dynamic supernodal solver is shown to exceed the performance of the conventional (BLAS-based) supernodal method for solving triangular systems. These methods are incorporated into CHOLMOD, a sparse Cholesky factorization and update/downdate package which forms the basis of x = A\b MATLAB when A is sparse and symmetric positive definite.
Funder
National Science Foundation
Publisher
Association for Computing Machinery (ACM)
Subject
Applied Mathematics,Software
Cited by
88 articles.
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