Even fresher numbers to crunch

Numerical Algorithms Group (NAG) adds new features to Java dev library

Lucy Carey
numbers1

“Probably the biggest” trove of mathematical and statistical algorithms available for developers undergoes its second Java-flavoured revamp.

 

If mathematical and statistical algorithms are what
get your juices pumping, you’re in for a figurative treat. The
Numerical Algorithms Group (NAG) – an Anglo/US organization
which churns out numerical software and compilers for devs who want
to incorporate mathematical or statistical functionality into their
applications – has just announced an update of over 100 routines in
the NAG Library
for Java
.

The NAG Library is quite probably the biggest repository of
mathematical and statistical algorithms around. Its remit extends
to impressive sounding things such as linear algebra,
optimization,
quadrature,
the solution of ordinary
and partial
differential equations
, regression
analysis
, and time series
analysis
. People working in fields like financial analysis,
business analytics, science, engineering and research tap into the
NAG to access problem solving algorithms for their daily
activities.

This new release is the 24th in its 40 year history,
and only the second time the Java Library has undergone a tune up.
Bundled inside it are a stack of new features, including
multi-start (global) optimization, non-negative least squares
(local optimization), nearest correlation matrix, inhomogeneous
time series, Gaussian mixture model, confluent hypergeometric
function (1F1), Brownian Bridge & random fields, best subsets,
real sparse eigenproblems, matrix functions, and two-stage spline
approximation.

Launched in the spring of last year, the NAG Library for Java’s key goal
is to provide Java devs with the newest tried and tested NAG
Library routines. Instead of puzzling out their own formulae,
coders can tap into this resource and speedily access, “robust,
stringently tested and fully documented numerical code,” making the
development process zippier and less susceptible to errors.

Key capabilities within the NAG Library for Java
numerical routines include: Optimization, (Local and Global),
linear, quadratic, integer and nonlinear programming, ordinary and
partial differential equations and Wavelet Transforms, among other
complicated things. In terms of statistical routines, Java devs can
access functions such as random number generation, analysis of
variance and contingency table analysis, and time series
analysis.

Along with Java, the NAG also churns out mathematical solutions
for C/C++, Fortran, Python, MATLAB, C#, F#, and R, as well as
multicore and Symmetric Multiprocessor (SMP) computers, and
distributed memory systems and banks of workstations and PCs.

Image by See-ming Lee

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