NAGnews 138

In this issue:

Changepoint Analysis using MongoDB and the NAG Library for Python

The following is taken from a recent NAG Blog post by John Muddle, NAG Technical Sales Support Engineer.

Recently, I attended the Alan Tayler Day at St. Catherine's College, Oxford, organised by the Smith Institute. One of the speakers was Dr Rebecca Killick, of Lancaster University, whose talk highlighted her collaboration with NAG that has led to the inclusion of the PELT algorithm into the NAG Library.

The PELT algorithm, of Killick et al, is designed to detect changepoints in an ordered sequence of data, for example, a time series. A changepoint is the location in the series (or time) at which one or more properties of the sequence, such as the mean, changes. A typical example of this is the time at which the average price of a stock changes to a new average value; this is an example that we will consider in more detail later in this post. PELT is an acronym for Pruned Exact Linear Time, where pruned stands for the pruning technique applied to the data to reduce the computation cost. Exact, in this situation, stands for the nature of the search algorithm for the changepoints; it is guaranteed to find the exact minimization of a cost function used to determine the changepoints. Finally, the algorithm is linear in time, this means that, as long as the number of changepoints grows linearly, the cost of the algorithm is linear O(n), where n is the number of data points. More information about the PELT algorithm can be found in our documentation and Killick et al. (2012).

Read John's blog post here.

POP! Software Optimisation Support for EU Organisations

NAG are delighted to be part of the EU group called POP (Performance Optimisation and Productivity) that is helping to improve the performance of software. In brief POP offers to analyse software and recommend improvements with a focus on HPC and parallelisation. This service is free of charge to EU organisations.

We have the tools and expertise to analyse all aspects of code performance from individual CPUs up to inter-processor communications, for example successfully identifying memory bottlenecks and load imbalances for existing clients, allowing a better understanding of execution efficiency and targets for code refactoring.

We're currently identifying new clients for whom software performance improvements could lead to benefits for the organisation or user community. To express an interest please contact or see the POP (Performance Optimisation and Productivity) website for more information.

Students at The University of Manchester receive their NAG Awards

Dr Craig Lucas, NAG Senior Technical Consultant, was honoured to present the latest set of NAG Student Awards to exemplary Maths students at The University of Manchester recently.

NAG Student Prize Winner Adam Crowder
© photo copyright Gareth Wyn Jones

The photo shows Craig awarding Adam Crowder the Prize for MSc in Applied Mathematics with Numerical Analysis, at the Applied Maths Industry Day that took place in February. Adam is now doing a PhD at Manchester with Catherine Powell in "adaptive algorithms for the stochastic Galerkin finite element method". The winner for the NAG Award for MSc Mathematical Finance was Zhanbin Du, now based in China. Well done to both highly deserving winners and we look forward to hearing of your future successes.

The Code Contributors - future proof your algorithmic code with the NAG Library

Collaboration is at the heart of NAG and the NAG Library, with hundreds of algorithms having been contributed by people all over the world. Last year we interviewed some past and present 'code contributors' to learn about the process and what it means to them.

The interviews feature in this piece. In this NAGnews we ask Klaus Schittkowski, Professor at The University of Bayreauth to tell us a bit about his specific contribution.

The code MISQP (Mixed-Integer Sequential Quadratic Programming) which entered the NAG Library at Mark 25, is based on a completely new mathematical approach to solve nonlinear mixed-integer problems. Its development has been sponsored by Shell over three years since they found out that for a large class of practical mixed-integer optimizations problems, the existing algorithms are either not applicable (e.g. require relaxable integer variables, analytical model formulations,...) or are by far too expensive in terms of number of function evaluations, the main performance criterion in many engineering applications. Learn more.

Best of the Blog

Inspiring Future Talent - International Women's Day 

NAG marked International Women's Day 2016, by publishing an interview with Placement Student, Heather Briggs. Heather is working at NAG for a year during her Mphys course, an integrated Masters degree. In the interview she speaks about her time at school, what led to her degree choice, and the challenges and highlights she has experienced along the way. Read Heather's interview here.

Out & About with NAG

Come and see us at various conferences and events over the next few months.

  • The Trading Show 22-23 March 2016, London
  • IDC HPC User Forum
    11-13 April 2016, Tuscon
  • QuanTech Conference
    21-22 April 2016, London
  • Global Derivatives Trading & Risk Management
    9-13 May 2016, Budapest
    (we are pleased to offer NAGnews subscribers a 25% discount on delegate passes. Visit the website and quote VIP code: FKN2466NAGW)
  • The Trading Show Chicago
    18-19 May 2016, Chicago
    NAGnews subscribers will receive 15% off the current price to attend. Register and enter the voucher code 'NAG' to activate your discount.
  • PyCon 2016
    29-31 May 2016, Oregon

NAGnews - Past Issues

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