New algorithms for application developers available in the latest NAG Library
6 May 2015 – The Numerical Algorithms Group (NAG), provider of numerical and HPC services and solutions announces availability of the latest NAG Library including over 80 new mathematical and statistical algorithms. The new functionality included at Mark 25 of the Library brings the number of available functions to over 1,800, all of which are expertly documented and includes extensions in the areas of Change Point Analysis, LARS / LASSO / Forward Stagewise Regression, Mixed Integer Nonlinear Programming, Nearest Correlation Matrix, Unscented Kalman Filter, plus a new OpenMP Utilities chapter.
The new NAG Library contains additional functions that have been added in direct response to customer requests, including requests from major banks, market intelligence companies and several major Universities. The Library also includes further enhancements contributed by NAG’s expert developers and collaborators.
New mathematical and statistical content:
- Many new Matrix Functions
- Least Angle Regression (LARS), Least Absolute Shrinkage and Selection Operator (LASSO) and Forward Stagewise Regression
- Nearest Correlation Matrix updates
- Unscented Kalman Filter
- Change Point Analysis
- High Dimensional Quadrature using Sparse Grids
- Bandwidth Reduction of Sparse Matrix by Reverse Cuthill-McKee Reordering
- Solutions to the classical Travelling Salesman Problem
- OpenMP Utilities
The inherent flexibility of the mathematical and statistical functions in the NAG Library enable it to be used across multiple programming languages, environments and operating systems including C and C++, Excel, Java, Microsoft .NET, Python, Visual Basic, Fortran and many more.
John Holden, NAG’s VP Global Markets commenting on the new release “I am delighted to see the new content addressing customer demand. It is particularly pleasing to see the content being implemented in collaboration with our customers and academic partners. NAG continues to collaborate with leading scientists from around the world. With Mark 25 we welcome to the NAG family Prof Klaus Schittkowski, University of Bayreuth and Dr Rebecca Killick, Lancaster University.”
Who uses the NAG Library
The algorithms available in the NAG Library are used in applications and computing solutions by thousands of organisations around the world. Aerospace designers, Biologists, Financial Engineers, Meteorologists, Road Planners, Chemists, Data Scientists, Risk Analysts, Oil Field Modellers as well as Academic Researchers in many disciplines, all rely on NAG; they implicitly trust their results because of the innate quality of the NAG Library.
More benefits of the NAG Library:
· Highly detailed documentation giving background information and function specification. In addition it guides users, via decision trees, to the right function to solve their problem. Example programs are included for all routines which include test data and results.
· Expert Support Service direct from NAG’s algorithm development team – if users need help, NAG’s development team are on hand to offer assistance.
· Example programs are included in the Library to help users get started with its functions. If a specific example program requires any input data a helpful expected results file is available.
The Numerical Algorithms Group (NAG) applies its unique expertise in numerical engineering to delivering high-quality computational software, consultancy and high performance computing services. For over 40 years NAG experts have worked closely with world-leading researchers in academia and industry to provide powerful, reliable and flexible software and solutions relied on by tens of thousands of individual users, as well as numerous independent software vendors. NAG is a not-for-profit organization and serves its customers from offices in Oxford, Manchester, Chicago and Tokyo, through staff in France and Germany, as well as via a global network of distributors.
Together with the globally renowned NAG Library, NAG experts assist others in many ways, whether that be by parallelizing serial codes so they optimally perform on HPC hardware to delivering in-depth technical training courses through developing bespoke algorithms.