Computer Solution of Large Sparse Positive Definite
Computer Solution of Large Sparse Positive Definite
A compact row storage scheme for Cholesky factors using elimination trees
ACM Transactions on Mathematical Software (TOMS)
On the storage requirement in the out-of-core multifrontal method for sparse factorization
ACM Transactions on Mathematical Software (TOMS)
On threshold pivoting in the multifrontal method for sparse indefinite systems
ACM Transactions on Mathematical Software (TOMS)
Pairwise Reduction for the Direct, Parallel Solution of Sparse, Unsymmetric Sets of Linear Equations
IEEE Transactions on Computers
A benchmark package for sparse matrix computations
ICS '88 Proceedings of the 2nd international conference on Supercomputing
ACM Transactions on Mathematical Software (TOMS)
A graph partitioning algorithm by node separators
ACM Transactions on Mathematical Software (TOMS)
Reference history, page size, and migration daemons in local/remote architectures
ASPLOS III Proceedings of the third international conference on Architectural support for programming languages and operating systems
The influence of relaxed supernode partitions on the multifrontal method
ACM Transactions on Mathematical Software (TOMS)
The multifrontal method and paging in sparse Cholesky factorization
ACM Transactions on Mathematical Software (TOMS)
A generalized envelope method for sparse factorization by rows
ACM Transactions on Mathematical Software (TOMS)
PARASPICE: a parallel circuit simulator for shared-memory multiprocessors
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Modification of the minimum-degree algorithm by multiple elimination
ACM Transactions on Mathematical Software (TOMS)
A partial pivoting strategy for sparse symmetric matrix decomposition
ACM Transactions on Mathematical Software (TOMS)
Numerical experiments with SPARSPAK
ACM SIGNUM Newsletter
| Hi-index | 0.00 |
The development, analysis and production of algorithms in sparse linear algebra often requires the use of test problems to demonstrate the effectiveness and applicability of the algorithms. Many algorithms have been developed in the context of specific application areas and have been tested in the context of sets of test problems collected by the developers. Comparisons of algorithms across application areas and comparisons between algorithms has often been incomplete, due to the lack of a comprehensive set of test problems. Additionally we believe that a comprehensive set of test problems will lead to a better understanding of the range of structures in sparse matrix problems and thence to better classification and development of algorithms. We have agreed to sponsor and maintain a general library of sparse matrix test problems, available on request to anyone for a nominal fee to cover postal charges. Contributors to the library will, of course, receive a free copy.