A new set of Derivative-free Optimization solvers has been integrated into the NAG Library at Mark 27. They are aimed at optimizing Black Box models and can handle either calibration (nonlinear least squares) problems or problems with a generic objective function. The solvers, available with both direct and reverse communication interfaces should show an improved convergence rate compared to the existing DFO solutions in the NAG Library.
For more information on the new Derivative-free Optimization solvers see the mini-article and Technical Poster. There's also a wealth of information in the 'Minimizing or Maximizing a Function' NAG Library Chapter Introduction here.
Free trials of the new NAG Library are available.
We recommend that users move to the latest NAG Library because of additional functionality and guaranteed support levels (all supported clients are guaranteed technical assistance on the current release and one previous). If you have any questions about this release do contact your Account Manager or the NAG Technical Support Service.
Tape-based AD Libraries, such as NAG's dco/c++ tool, keep a record of calculations that are executed by a program in order to evaluate derivatives. They are applicable to a wider range of numerical codes than tape-free AD libraries, which are typically written to compute derivatives for a specific library of functions. The Stan Math Library is a tape-free AD library.
Philip Maybank, NAG Numerical Software Developer, recently presented at StanCon 2019.
The basic idea of the work in the presentation is that dco/c++ can be used to supply derivatives to Stan. This extends the range of functions which can be used by Stan's MCMC samplers. Philip illustrates this idea on a toy problem: inferring the parameters of a damped harmonic oscillator driven by white noise using Stan's NUTS.
In a recent article by Anthony Malakian, Editor at Large, at WatersTechnology, he discovers how a new technology stack has enabled significant speed-ups for Scotiabank in calculating its valuation adjustments.
Using a technology stack of cloud GPUs, provided by Microsoft Azure, NAG Algorithmic Differentiation software tools, dco/c++, dco/map and the NAG AD Library, and Origami, a Grid/Cloud Task Execution Framework provided by Xi-FINTIQ and NAG in partnership, Scotiabank achieved an impressive runtime speedup of x30 for their risk calculations and derivatives pricing. In the words of Anthony Malakian this "allows brokers to deliver more accurate derivatives pricing in 20 seconds, which would previously have taken 10 minutes".
NAG is a proud partner of the EU POP project, and via POP, regularly presents learning webinars around application performance. Over the last year or so the POP Team, collaborators and users have presented some really insightful sessions. All were recorded and can be viewed by clicking on the links below.
- Using OpenMP Tasking
- How to Improve the Performance of Parallel Codes
- Exascale Matrix Factorization: Using Supercomputers and Machine Learning for Drug Discovery
- Software for Linear Algebra Targeting Exascale SLATE project
POP offers a unique free of charge service to EU organisations that helps analyse software and recommend improvements with a focus on HPC and parallelization.
4th EAGE Workshop on HPC for Upstream
Dubai, 7-9 October 2019
Mathematical Optimization Seminar at Teratec
Bruyères-le-Chatel, 10 October 2019
The 15th WBS Quantitative Finance Conference
Rome, 16-18 October 2019
The Trading Show Europe
London, 17 October 2019
Quant Insights
London, 15 November 2019
SC19
Denver, 17-19 November 2019
