Fundamental to NAG's longevity and success has been its ethos for collaboration and support of research, development and learning. NAG's expertise, gained through the provision and support of its globally renowned numerical libraries and HPC services, is the cornerstone upon which all projects and research into future technologies and products is based.
This area of the NAG website is the repository for information relating to research currently being undertaken by NAG, prototype products available to beta test and links to white papers and technical reports.
The following details the innovative projects currently underway at NAG. Follow the links to learn more about the activities that may evolve into new products and functionality.
The NAG Library for .NET, currently at beta release, has been developed to serve the growing number of application developers and users of Microsoft .NET requiring mathematical and statistical routines. The numerical routines in the NAG Library for .NET are fast and efficient in execution which can enhance application capabilities and reduce crucial development time.
The NAG Numerical Routines for GPUs prototype is a set of fully functional parallelized numerical routines which, when used on Graphics Processing Unit (GPU) architecture, result in significant performance gains compared to traditional CPU systems. Learn more.
ADVISE (Analysis of Data in a VISual Environment) is a collaborative research project which is developing a new toolkit for visualization and analysis. By merging statistical and visualization methods throughout the exploration process, ADVISE will provide insight into the increasingly large and complex datasets that now occur routinely in many application areas.
NAG and the School of Mathematics of the University of Manchester are jointly funding a PhD project to investigate “Optimization Problems in Structured Numerical Linear Algebra”. The project concerns the development of theory, algorithms and software for numerical linear algebra problems in which the matrices have structure. The problems of interest are of the form “find a structured matrix optimizing some objective function”. The objective is typically the distance from a given matrix to a matrix in some structured set. One problem being looked at is finding the nearest correlation matrix with k-factor structure to a given matrix, where k-factor structure means that the off-diagonal of the correlation matrix agrees with the off-diagonal of a rank k matrix. This problem arises in credit basket applications in finance.
If you would like to talk to us about our current research projects, or about future product directions please email us or telephone. If you would like to see specific functionality within the NAG Libraries please provide details using this form here.