Issue 83, 17 September 2009
- NAG Toolbox for MATLAB®, Mark 22 now available
- New NAG White Paper: Calling NAG routines from Scilab
- Nearest Correlation Matrix in the NAG Library and NAG Toolbox for MATLAB® explained
- NAG and Python Part 3: Callback functions
- Supercomputing: Technical computing throws a new challenge to science budgets
- NAG Technical Tip: Using the NAG Toolbox for MATLAB®
- Out & About with NAG
- New NAG product implementations
NAG Toolbox for MATLAB, Mark 22 now available
NAG is delighted to announce the availability of the first implementations for Mark 22 of the NAG Toolbox for MATLAB. The Toolbox is the largest and most comprehensive single numerical toolkit that both complements and enhances MATLAB. At Mark 22, the NAG Toolbox for MATLAB contains 1,415 functions that provide solutions to a vast range of mathematical and statistical problems. The functionality contained within this toolbox gives, for the first time, a 'one-stop' solution to your numerical computational needs.
If you are supported user of the NAG Library you might be entitled to receive the NAG Toolbox for MATLAB as part of your licence agreement with NAG. If you'd like us to check your entitlement simply email firstname.lastname@example.org.
New NAG White Paper: Calling NAG Routines from Scilab
Scilab is an open source scientific software package for numerical computation. NAG's latest white paper details how to call NAG Library routines, which can provide significant mathematical and statistical enhancements, from Scilab (http://www.scilab.org/ ).
NAG Nearest Correlation Matrix in the NAG Library and NAG Toolbox for MATLAB explained
In the extensive new functionality included at Mark 22 of the NAG Fortran Library and the NAG Toolbox for MATLAB is a Nearest Correlation Matrix routine. In this mini-article we take a closer look at Nearest Correlation Matrix and give background to how it came to be included in the NAG Library and NAG Toolbox for MATLAB.
A correlation matrix is characterised as being a real, square matrix with ones on the diagonal and with nonnegative eigenvalues. A matrix with non-negative eigenvalues is called positive semi-definite.
If a matrix C is a correlation matrix then the elements of C, C(I,J) represent the pair-wise correlation of entity I with entity J, that is, the strength and direction of a linear relationship between two.
In the literature there are numerous examples illustrating the use of correlation matrices but the one we have encountered the most arises in finance where the correlation between various stocks is used to construct sensible portfolios. On our website Simon Acomb talks about general use of correlation in finance.
Click here to read the mini-article in full.
NAG and Python Part 3: Callback functions
Following on from NAG's series of articles highlighting the inherent flexibility of NAG's numerical routines, NAG enthusiast, Dr Mike Croucher of the University of Manchester has expanded his investigations of linking routines in the Python language.
Part 3 of the series considers callback functions. "A callback is executable code that is passed as an argument to other code. For example, say you want to want to find the zero of the function exp(-x)/x using a routine from the NAG library. Your first step would be to create a function that evaluates exp(-x)/x for any given x. You'll later pass this function to the NAG routine to allow the routine to evaluate exp(-x)/x wherever it needs to.
Let's take a look at some Python code that performs the calculation described above. The NAG routine I am using locates a zero of a continuous function in a given interval by a combination of the methods of linear interpolation, extrapolation and bisection and is called c05adc."
For many years, scientists have been able to rely on computer technology doubling in speed roughly every 18 months ' usually referred to as a result of Moore's Law. To increase the speed or capability of the simulations used in your research, all you had to do was buy updated hardware every year or so. Increased application performance and measurable productivity increases were straightforward and predictable results of periodic hardware upgrade investments.
Of course, it is now well known and publicized that this "free lunch" of easy increases in science computing speed from buying new hardware has ended. The Moore's Law-driven hardware race of the past few decades is not the major fuel for performance gains in today's most successful HPC centers. Now, performance increases ' sometimes on the orders-of-magnitude scale ' come from re-engineering software for scalability and from better matching of hardware and software. It's all about enhanced parallel software engineering -- parallel, scalable and robust code tuned to the new world of manycore architectures.
The new twist on Moore's Law ' roughly twice as much parallelism every 18 months ' means that investments in software engineering can be the make-or-break factor. A side issue of this development is that it is renewing focus on the entire HPC ecosystem and the debate on how the "P" of HPC should be defined.
Continue reading Andrew Jones' article "Technical Computing Throws a New Challenge to Science Budgets" here.
NAG Technical Tip: Using the NAG Toolbox for MATLAB
Some of the most useful and powerful routines in the NAG Toolbox for MATLAB require the user to provide an M-file specifying a function. For example optimisation routines require the user to provide the function to be minimised. Ordinary differential equation routines and quadrature routines also require the user to provide functions in order to specify the problem.
Read the full technical tip here including code examples.
Out & About with NAG
21 - 25 September 2009, Mainz, Germany
NAG experts will be present at BASTA! NAG will be showcasing the flexibility of the NAG Library and the new NAG Library for .NET prototype. BASTA! is one of the largest exhibitions in Europe focussing on .NET, Visual Studio and other programming environments. Topics in the speaker line-up include Cloud Computing, Multicore Programming plus much more.
- NVIDIA GPU Technology Conference
30 September ' 2 October 2009, San Jose, USA
Ian Reid, Chief Commercial Officer at NAG is presenting "Banking on Monte Carlo" at this prestigious conference on 1st October 2009.
- Risk USA
26-29 October 2009, New York, USA
NAG will be exhibiting and sponsoring Risk Magazine's 15th Annual Risk USA Conference, the USA's premier Risk Management event for financial institutions.
- HECToR (High End Computing Terascale Resource) Training Courses
Presented by the NAG HECToR Team
A full list of forthcoming HECToR Training Courses can be viewed on the official HECToR website here.
For more information on any of the above events visit NAG's ‘Out & About’ webpage
New NAG product implementations
The NAG Toolbox for MATLAB, Mark 22 is now available for the following platforms:
- Linux 32-bit
- Linux 64-bit
- Windows 32-bit and 64-bit coming soon
For full details of these and all other available implementations, visit the NAG site. Comprehensive technical details of each implementation are given in the relevant Installation and User Notes at http://www.nag.co.uk/doc/inun.asp.
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