NAGnews 143

Posted on
20 Oct 2016

In this issue:

NAG Library, Mark 26 announced featuring the new NAG Optimization Modelling Suite

Today we are delighted to announce the new additions to our flagship product, the NAG Library. Mark 26 brings a multitude of new functionality including Optimization solvers in the NAG Optimization Modelling Suite. The new Library also features additional routines in existing areas of Nearest Correlation Matrix, Quadrature, Least Squares and Eigenvalue Problems (LAPACK) and OpenMP Utilities.

The NAG Optimization Modelling Suite has been introduced to better tackle the input of complex problems without forming difficult interfaces with a daunting number of arguments. It is available for the new optimization solvers introduced at this mark; the semidefinite programming solver and the interior point method for nonlinear optimization. However, the suite will expand in the future to cover more problem types.

Mark 26 new functionality highlights:

  • Interior Point Method for large-scale nonlinear programming problems (accessible from the new Optimization Modelling Suite)
  • Linear and Nonlinear Semidefinite Programming Solver
  • Gaussian Quadrature
  • Nearest Correlation Matrix Functions
  • Least Squares and Eigenvalue Problems (LAPACK)
  • OpenMP Utilities

How can I start using Mark 26?

Many readers of NAGnews will be entitled to use Mark 26 as part of their supported NAG Software Agreement. If you currently use the NAG Library and would like us to see if you are eligible for an upgrade to the new Mark, email us and we'll do the checking. If you're interested in using the routines in the Library do get in touch or visit our website for more information.

Mark 26 Algorithm Spotlight: NEW Semidefinite Programming in the NAG Library

New to the NAG Library, Mark 26, is a linear and nonlinear semidefinite programming (SDP) solver, created in collaboration with the University of Birmingham, UK. The new type of SDP constraints, matrix inequalities, allows users to address a completely new set of problems or to express some existing problems in a whole new way. In addition, our nonlinear semidefinite programming (SDP) with bilinear matrix inequalities, is the only supported commercial solver in the world. This type of problem is especially important in system and control theory.

The NAG technical lead for this new solver has written a mini-article explaining more about this functionality. You can read it here.

Calling NAG Routines from Julia

Julia Computing was founded in 2015 by the co-authors of the Julia programming language to help private businesses, government agencies and others develop and implement Julia-based solutions to their big data and analytics problems.

Julia is an open-source language for high-performance technical computing created by some of the best minds in mathematical and statistical computing.

Reid Atcheson, Accelerator Software Engineer at NAG and Andy Greenwell, Senior Application Engineer, Julia Computing have teamed up to ensure that NAG Library routines can be called from the Julia language. Read their blog here.

Learning opportunity: 'Improving Application Performance on Intel® Xeon Phi™ Processor' training course and webinar sessions

NAG and Intel are partnering to present a highly valuable set of learning opportunities designed to teach the fundamental skills needed to achieve optimum application performance on the Intel® Xeon Phi™ Processor architecture.

We are providing in-person courses as well as the same content delivered by webinar series. The courses will deliver targeted instruction in both theory and practical sessions.

Delegates will:

  • Increase their knowledge of the Intel® Xeon Phi™ Processor architecture and what applications can best leverage it
  • How to use OpenMP to utilize multicore parallelism as well as vectorization
  • How to further optimize already-parallel applications to even more effectively utilize Intel® Xeon Phi™ Processor and maximize performance
  • Take an initial application and optimize it for excellent performance on Intel® Xeon Phi™ Processor

Click here for more information and to register your interest:
2-day training course at Yale University - 26 & 27 October 2016
2 hour, 7 days webinar series - 1-9 November 2016

Remembering James Hardy Wilkinson by Sven Hammarling and Nick Higham

We are proud to be so closely associated with the eminent numerical analyst Jim Wilkinson. In their piece featured on the SIAM News Blog entitled 'Remembering James Hardy Wilkinson', Sven Hammarling, NAG Honorarium, and Professor Nick Higham, University of Manchester, remember him.

Best of the Blog

NAG and Cloudera: How to call NAG Library routines from Cloudera using Spark

NAG has recently announced a new partnership with Cloudera. Cloudera is a software company that provides Apache Hadoop-based software, support and services, and training to business customers. Cloudera's open-source Apache Hadoop distribution targets enterprise-class deployments of that technology. Cloudera also donates engineering output upstream to the various Apache-licensed open source projects (Apache Hive, Apache Avro, Apache HBase and so on) that combine to form the Hadoop platform. Cloudera is also a sponsor of the Apache Software Foundation. The first fruits from our partnership is the engineering of the NAG Library algorithms so they can be called from Cloudera to enhance the accuracy and speed of processing for a range of problem types. NAG are also able to provide advice about the most appropriate approach, using NAG routines or others, to solving numerical problems in a Cloudera Hadoop environment. To learn how to call NAG Library routines from Cloudera using Spark visit here, alternatively, if you have any questions simply email us and an expert will get back to you as soon as possible.

Out & About with NAG

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

13-18 November 2016, Salt Lake City

NAG experts will be at the event and are looking forward to talking to delegates.

Computing Insight UK 2016
14-15 December 2016, Manchester