Name
Abstract | ||
|---|---|---|
Double-double and Quad-double arithmetics are effective tools to reduce the round-off errors in floating-point arithmetic. However, the dense data structure for high-precision numbers in MuPAT/Scilab requires large amounts of memory and a great deal of the computation time. We implemented sparse data types ddsp and qdsp for double-double and quad-double numbers. We showed that sparse data structure for high-precision arithmetic is practically useful for solving a system of ill-conditioned linear equation to improve the convergence and obtain the accurate result in smaller computation time. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-55224-3_60 | PARALLEL PROCESSING AND APPLIED MATHEMATICS (PPAM 2013), PT I |
Keywords | Field | DocType |
Ill-conditioned matrix problem, Sparse matrix, Multiple precisions | Convergence (routing),Linear equation,Data structure,Computer science,Parallel computing,Arithmetic,Algorithm,Sparse matrix,Computation | Conference |
Volume | ISSN | Citations |
8384 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsubasa Saito | 1 | 13 | 4.04 |
| Satoko Kikkawa | 2 | 1 | 0.77 |
| Emiko Ishiwata | 3 | 34 | 9.71 |
| Hidehiko Hasegawa | 4 | 27 | 5.83 |