High-precision floating-point arithmetic in scientific - download pdf or read online

By Bailey D.H

Show description

Read Online or Download High-precision floating-point arithmetic in scientific computation PDF

Similar computational mathematicsematics books

Augmented Lagrangian Methods: Applications to the Numerical - download pdf or read online

The aim of this quantity is to offer the foundations of the Augmented Lagrangian strategy, including quite a few purposes of this technique to the numerical answer of boundary-value difficulties for partial differential equations or inequalities bobbing up in Mathematical Physics, within the Mechanics of continuing Media and within the Engineering Sciences.

Download PDF by Bijan Mohammadi, Olivier Pironneau: Applied Shape Optimization for Fluids, Second Edition

Computational fluid dynamics (CFD) and optimum form layout (OSD) are of useful significance for lots of engineering purposes - the aeronautic, motor vehicle, and nuclear industries are all significant clients of those applied sciences. Giving the state-of-the-art healthy optimization for a longer variety of functions, this re-creation explains the equations had to comprehend OSD difficulties for fluids (Euler and Navier Strokes, but additionally these for microfluids) and covers numerical simulation strategies.

Extra resources for High-precision floating-point arithmetic in scientific computation

Example text

N. Cristianini and J. S. Taylor, An Introduction to Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University Press, New York, 2000. 47. S. Sch¨olkopf and A. Smola, Learning with Kernels: Support Vector Machines,Regularization, Optimization and Beyond, MIT Press, Cambridge, MA, 2002. 48. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, New York, 2004. 49. S. Mika, G. R¨atsch, J. Weston, B. -R. -H. Hu, J. Larsen, E. Wilson, and S.

Wang and X. Tang, A unified framework for subspace face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(9):1222–1228, 2004. 24. J. Ye, Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems, J. Mach. Learning Res. 6:483–502, 2005. 25. H. Park, M. Jeon, and J. B. Rosen, Lower dimensional representation of text data based on centroids and least squares, BIT 43(2):1–22, 2003. 26. P. Howland, M. Jeon, and H. Park, Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition, SIAM J.

Royal Stat. Soc. Ser. B (1):267–288, 1996. 69. L. Wang and X. Shen, On L1 -norm multiclass support vector machines: Methodology and theory, J. Am. Stat. Assoc. 102(478):583–594, 2007. 70. J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, 1-Norm support vector machines, in Advances in Neural Information Processing Systems, 2003. 71. J. Ye, J. Chen, R. Janardan, and S. Kumar, Developmental stage annotation of Drosophila gene expression pattern images via an entire solution path for LDA, in ACM Transactions on Knowledge Discovery from Data, Special Issue on Bioinformatics, 2008.

Download PDF sample

High-precision floating-point arithmetic in scientific computation by Bailey D.H

by William

Rated 4.74 of 5 – based on 10 votes