NAGnews 178
Quick and Accurate Polynomial Root-Finding With New NAG® Library Solvers

Finding the zeros of a polynomial is a long-standing problem in mathematics, with applications in finance, physics, engineering, control theory, signal processing, …the list goes on. It is tempting to think that such an old and classical problem must have been completely solved by now, however, this is far from the case. In his latest blog, NAG’s Edvin Hopkins demonstrates how to increase the accuracy of your results, reduce development time and massively increase scaling for larger problems with the new NAG® Library polynomial root-finding solvers. 

How to access the new NAG® Library functionality

As with all new releases, we encourage NAG® Library users to upgrade to the latest Mark to access the new content and performance improvements. The new NAG® Library functionality is also available in the NAG® Library for Python.

If you don’t have access to the NAG® Library and you’d like to try the new functionality, we offer full product trials. If you have any questions or need help, do get in touch with our Technical Support team

7 Sins of Mathematical Optimization (and How to Avoid Them)!

LIVE WEBINAR | 15 JULY 2021: Join NAG’s mathematical optimization team as they present the 7 mathematical optimization sins, resulting in errors, inefficiency, and increased costs. Learn how to avoid common pitfalls when using mathematical optimization techniques, and how to get the most from optimization solvers! 

Increase Machine Learning ROI and Accelerate Training With a High Performance Storage Solution Set-up

High performance, robust storage is critical to improving machine learning ROI and performance metrics. In collaboration with NVIDIA and Azure ─ NAG’s Phil Tooley guides you through a storage solution set-up using Thinkparq’s BeeOND filesystem. Phil shares machine learning performance benchmarks showing the vast performance improvements with more efficient compute node utilisation. 

Faster Heston Model Calibration using DFO Techniques

In the latest Optimization Corner blog series, Benjamin Marteau demonstrates a new technique for the calibration of black-box models. On a Heston calibration model, using a derivative-free optimization solver, he saves more than 70% function evaluations, while achieving better convergence. Read the blog to learn how NAG® Library DFO solvers can be beneficial for your black-box problems. 

Optimization Corner
Implementation Challenges of SA CVA – Aligning People, Processes and Technology

Catch the Recording

If you missed our May 2021 webinar you can catch a recording of “Implementation challenges of SA CVA – Aligning people, processes and technology” here.

Webinar Summary

The countdown has already begun for SA-CVA compliance. By 2023, revised Basel 'IV' market risk (FRTB) and CVA frameworks are required for implementation. But where do you begin? What are the issues and challenges you'll face along the way?

This webinar provides regulatory, risk and quantitative insights to ensure you deliver your SA-CVA regulatory changes against challenging deadlines.

Covering:

  • Understand the size of the implementation task and timelines for delivery
  • Ways to leverage existing regulations for successful SA-CVA
  • Jurisdictional variance and how it can impact implementation
  • Wider implications across the bank
  • Preparation for SA-CVA compliance
  • The role of the XVA trading desk 
  • Reviewing quantitative requirements for SA-CVA
  • Exploring the competitive advantage of SA-CVA and implications for capital requirements, hedging and pricing on derivatives
NAG Sponsored Student Awards

We are delighted to congratulate the latest winners!

June 2021 | STFC CoSec Impact Award 

  • Winner: Ryan Warr, University of Manchester
  • Runner-up 2: Dr. Antoni Wrobel, The Francis Crick Institute
  • Joint Runner-up 3: Angela Harper, University of Cambridge
  • Joint Runner-up 3: Dr. Palak Wadhwa, University of Leeds

December 2021 | Best performance EPSRC CDT in Fluid Dynamics, University of Leeds 

  • Winner: Joseph Myers Hill