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Pick of the Week: NAG Toolbox for MATLAB Opens to NAG Library Engineers using MATLAB gain advanced numerical capabilities via access to NAG's 1300+ computational routines. | Published June 25, 2008 Dear Desktop Engineering Reader:
The NAG Toolbox for MATLAB is a single source for more than 1,300 mathematical and statistical algorithms from the NAG Library. It integrates with MATLAB readily, making world-class Bessel functions, linear and quadratic optimizations, multivariate methods, nonlinear equations, random number generators, PDEs, and functions that are so specialized that you never imagined that you'd find them ready for you when you actually do need them. For that matter, the NAG Toolbox for MATLAB includes a number of algorithms that just aren't available in other commercial MATLAB toolboxes. An especially cool part of the NAG Toolbox for MATLAB is its interactive documentation. Not only does the Toolbox contain in-depth information, but it helps you pick the right algorithm for the job quickly. It also includes example MATLAB code showing you how to call the NAG routine. You can learn more about that in today's Pick of the Week write-up, where you can find links to product demos showing you all about this. You'll also find a link in the write-up to a fully functional trial download. NAG has been a leader in the development of software components for mathematics, modeling, optimization, statistics, visualization, and data mining since before a lot of you were born — 1970, to be precise. It is always popping up somewhere mind-blowing, such as with HECToR, the UK's new high-end computing resource located at the University of Edinburgh's Advanced Computing Facility. There, NAG now provides computational science and engineering support to the project after years of benchmarking technical selections. (Look for the link at the end of today's write-up for more on NAG and HECToR.) And mind-blowing expertise like that sums up the NAG Toolbox for MATLAB, which holds more precision components that will increase your application prototyping productivity than anything I know of. Considering that outfits like Argonne, CERN, Lawrence Livermore National Laboratory, and Lockheed-Martin rely on NAG algorithms, I assume that I'm not alone in that assessment. Thanks, Pal. — Lockwood
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