NAGnews 128 | 22 January 2015

Posted on
22 Jan 2015

In this issue


NAG and Red Oak Consulting Support KAUST Petascale Supercomputer Project


In December last year NAG and Red Oak Consulting announced their latest High Performance Computing (HPC) success by together providing a key component of a large supercomputer project by King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. KAUST benefited from NAG and Red Oak consulting during the procurement and planning process of the project, and will continue to do so into the commissioning and acceptance phases.

The team at NAG and Red Oak brought their track record of involvement in over 40 HPC procurement projects around the world and delivered impartial expert advice based on experience across the whole lifecycle, from supporting the drafting of the procurement documents, through datacentre planning, benchmarking, bid evaluation, and contract negotiations. The NAG and Red Oak team will continue to support the KAUST project through installation and commissioning, acceptance testing, and user support planning.

Read the entire news release here.


Adding a Slider Widget to Implied Volatility


Last year, NAG Technical Specialist Brian Spector, wrote a blog on Implied Volatility 'Implied Volatility using Python's Pandas Library' http://blog.nag.com/2013/10/implied-volatility-using-pythons-pandas.html where he downloaded real options data from the CBOE and calculated the volatility curve/surface. In a follow up to this popular post, Brian recently published 'Adding a Slider Widget to Implied Volatility' http://blog.nag.com/2015/01/adding-slider-widget-to-implied.html on the NAG Blog.

In this post he concentrates on the speed of calculating implied volatility via a variety of different methods. He looks at the volatility curve/surface using scipy, the NAG Library for Python, and the NAG C Library. In addition, he adds a slider widget to the Python graphs previously featured to see the real-time effects of changing the interest and dividend rates. All of the codes discussed in the blog can be downloaded to produce graphs, and a NAG licence is not required for the base case using scipy.optimize.fsolve.

Read the blog here: http://blog.nag.com/2015/01/adding-slider-widget-to-implied.html.


Mark 24 New Functionality Spotlight: New to the Wavelet Transforms Chapter


At Mark 24, the NAG Library has new functions for computing the three-dimensional discrete wavelet transform (DWT) and its inverse, including applying the DWT over multiple levels (c09f*). These functions are threaded for parallelism in some implementations. In addition there are new functions for inserting coefficients into and extracting coefficients from the compact form used in the multi-level two-dimensional functions and all three-dimensional functions, which will make working with the DWT functions easier as demonstrated in the examples. There are also functions for the maximal overlap discrete wavelet transform (MODWT) and its inverse in one dimension, which are useful in time series analysis.

To learn more about the recent routines in the Wavelet Transforms Chapter click here.


NAG Numerical Services assist a pharmaceutical company looking to forecast product lifecycle


A business team who are responsible for a line of chronic illness products, within a multinational pharmaceutical company, needed to consider a range of possible product lifecycle scenarios in order to plan investment returns and to avoid riskier financial strategies.

The team already had an Excel based solution and had started to develop new business based modelling approaches. These models needed to take into account both incremental growth and decline as well as allow for more sudden changes, such as delays in type approvals, loss of patents through legal dispute and unexpected epidemiological results.

They were looking for a partner who could provide guidance for the existing mathematical models and one who they could rely on as the business grew and modelling needs became more sophisticated.

NAG advised on the numerical detail of how best to incorporate the various data sets into the initial overall model and then to design and build new algorithms combined with existing NAG solvers.

The initial model worked successfully and there have been further iterations to refine and extend the techniques implemented, whilst maintaining the original user interface.

NAG routines were used to solve systems of nonlinear equations, to provide probability distributions and to perform optimization.

To read the full story visit NAG Numerical Services area here.


Training Courses & Events


NAG will be at the following exhibitions and conferences over the next few months.


NAGnews - Past Issues


We provide an online archive of past issues of NAGnews. For editions prior to 2010, please contact us.