Get the most out of your NAG Numerical and Statistical Routines within MATLAB®

Date & Time: Thursday 15th December 2016. 09:30 to 12:30

Location: Room G11, Sackville Street Building, Sackville Street, University of Manchester M1 3BU

Note: This course is restricted to attendees that are affiliated with Manchester University, e.g. PhD students, post-graduate students, members of staff, etc.

Maps & Travel: Information | Building Map (Building 1)

Cancellations and Queries: If you have registered and find that you are unable to attend or have questions about the course please email us.

Registration: Please use the form opposite to register.

The Numerical Algorithms Group (NAG) delivers high quality numerical software and high performance computing (HPC) services. NAG's Numerical Library, underpins thousands of applications used around the world in fields such as finance, science, engineering, academia, and research. Since its first release more than forty years ago, it has been widely trusted because of its unrivalled accuracy, reliability and portability, having been implemented on multiple platforms ranging from PC workstations to the world's largest supercomputers.

Members of the University of Manchester have been collaborating with NAG from its inception as authors and co-authors of routines in the Library. The NAG Library, and Fortran Compiler are available to University of Manchester staff and students for use on both institution and personal machines for academic use under a site-wide license.

The Library is accessible from several computing environments, including standard languages such as Fortran, C, C++, Python, Java, C# and Visual Basic, as well as packages like MATLAB® and Excel.

This training session will provide an overview of the NAG Numerical Library as a comprehensive collection of mathematical and statistical algorithms, focusing on the use of the NAG Toolbox for MATLAB. It will demonstrate the NAG Library's applicability to the solution of a number of numerical problems, including:

  • Local and global optimization;
  • Curve and surface fitting;
  • Statistical analysis of data;
  • Calculation of the nearest correlation matrix;
  • and many others.

It includes a number of hands-on exercises to facilitate attendees' familiarity with the workings and functionality of NAG algorithms.

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