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. © 2014 Springer-Verlag.
CITATION STYLE
Saito, T., Kikkawa, S., Ishiwata, E., & Hasegawa, H. (2014). Effectiveness of sparse data structure for double-double and quad-double arithmetics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8384 LNCS, pp. 643–651). Springer Verlag. https://doi.org/10.1007/978-3-642-55224-3_60
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