In this issue:
- New Optimization routines added to the NAG Optimization Modelling Suite (Mark 26.1)
- More technical detail about the Derivative-free Optimization for Data Fitting routine
- Latest Student Winner: Jonathan Hüser - 'Algorithmic Differentiation of Non-smooth and Discontinuous Functions'
- We're Hiring: Student Placement - Computational Software Engineer
- Blog Bites
- Conferences, Training Courses & Events
New Optimization routines added to the NAG Optimization Modelling Suite (Mark 26.1)
Two new Optimization routines have just been added to the NAG Optimization Modelling Suite within the NAG Library - Derivative-free Optimization for Data Fitting (thought to be the first such commercial solver available to the public in the world), and an Interior Point Method for Large Scale Linear Programming Problems.
The Derivative-free Optimization for Data Fitting routine was mentioned in a previous edition of NAGnews. The routine was developed in collaboration with the Centre for Doctoral Training in Industrially Focused Mathematical Modelling (InFoMM) at the University of Oxford. You can read about the collaboration in this blog.
NAG added its first derivative-free solver to the NAG Library approximately five years ago. Since then this field has attracted significant academic attention, resulting in numerous advances. The new Mark 26.1 Derivative-free Optimization Solver can effectively exploit the structure of calibration problems and we are excited to learn of its use by our users.
The new Interior Point Method for Large Scale Linear Programming Problems is built upon a very efficient sparse linear algebra package and actually implements two variants of interior point methods: the Primal-Dual and Self-Dual methods. The Primal-Duel usually offers the fastest convergence and is the default choice of solver. Both implementations should present significant improvements for large scale problems over the current LP/QP solvers in the Library, such as e04nq.
The NAG Optimization Modelling Suite was developed to better tackle the input of complex problems without forming difficult interfaces with a daunting number of arguments. It is available for the new two new optimization solvers mentioned above (26.1), and the semidefinite programming solver and the interior point method for nonlinear optimization introduced at Mark 26.
If you are an existing supported user of the NAG Library then it is likely that you can access the latest Optimization routines via upgrading your software. If you have any questions about this please don't hesitate to contact us.
Try the Library for 30 days with a full product trial. Apply here.
Derivative-free Optimization for Data Fitting routine (26.1) - more technical detail
Calibrating the parameters of complex numerical models to fit real world observations is one of the most common problems found in industry. At Mark 26.1 of the NAG Library we have introduced a model-based derivative-free solver (e04ff) able to exploit the structure of calibration problems. It is part of the NAG Optimization Modelling Suite which significantly simplifies the interface of the solver and related routines.
Read the technical mini-article relating to this new solver here.
Latest NAG Student Winner: Jonathan Hüser
We are delighted to announce our latest Student Prize Winner, Jonathan Hüser, for his excellent work shown here in the technical poster 'Algorithmic Differentiation of Non-smooth and Discontinuous Functions'.
If you are involved in teaching and are interested in NAG sponsoring an award at your learning institution do get in touch.
We're Hiring! Student Placement - Computational Software Engineer
We are looking for a Computational Software Engineer to undertake one or more projects within our Development Division. The post is based at NAG's head office in Oxford. The successful candidate will be mentored by an experienced member of staff, and will spend most of his or her time working in small team. At the end of the placement, she or he will have gained experience developing world-class technical software in a commercial setting.
For more information and details of how to apply see here.
My Year as a NAG Placement Student
My tenure as a Placement Student at NAG for my 'Year in Industry' has just come to an end, and I can say with certainty that it has been a very good and worthwhile experience. During my time here I was given the role of Software Engineer, which meant that I wasn't locked into any specific job. This allowed me to undertake a wide variety of tasks, such as implementing Struve functions, (a set of special functions), into the NAG Library, adapting NAG Library example programs for LAPACK and releasing them for everyone (not just NAG users), making improvements to the license key generation system, and more. Read More.
Does Forcing the Matrix to be Positive Definite Incur an Overhead?
Investigating and Comparing NAG Routines g02aa and g02ab
Alice Bentley recently undertook some work experience at NAG, and during her time here she looked at two of our Nearest Correlation Matrix routines, g02aa and g02ab. She wrote up her investigation and we are delighted to publish it on our blog. Read more.
Out & About with NAG
Exhibitions, Conferences and Trade Shows
The 13th Fixed Income Conference
18-20 October 2017, Florence
Readers of NAGnews can receive a 40% discount on the registration fee. To claim your discount use the code 'NAgFiC' in the special discount box.
Quant World Canada 2017
9 November 2017, Toronto
Quant Insights Conference
10 November 2017, London
The CQF and Wilmott present their 3rd annual Quant Insights conference, which will bring together leading practitioners to explore volatility modelling in financial markets with the keynote by leading quant expert Dr Paul Wilmott. Readers of NAGnews receive a 10% discount with code QI_NAG_17 - book your ticket
Super Computing 2017
12-17 November 2017, Denver