NAG LIBRARY FOR JAVA
NAG LIBRARY FOR JAVA
If you need to add mathematical and statistical functionality to your applications or if you have complex mathematical problems to solve, the NAG Library will provide a host of benefits. The NAG Library provides a solid numerical foundation and serves diverse mathematical areas. It is expertly documented, maintained and supported and is regularly updated with cutting edge algorithmic capabilities.
When Schroders were developing an in-house portfolio construction tool they massively reduced their development time by using NAG routines by creating 20 optimal portfolios in the same time that it used to take to create one. This gave them more time to focus on other value added areas. Learn more
We've selected key highlights from the NAG Library and show in more detail how a particular function or set of functions can be used. To learn more about a specific area/function click on the relevant link below.
- Second Order Cone Programming (SOCP) Mini Article, NEW Technical Poster NEW & GitHub Examples NEW
- Derivative-free Optimization Solver for Calibration Problems Technical Poster NEW & Mini Article NEW
- Nearest Correlation Matrix Technical Poster, NEW GitHub Examples NEW & Mini Article NEW
- Randomized Numerical Linear Algebra (RNLA) Algorithms NEW
- Non-negative Matrix Factorization for Analysing High-dimensional Datasets Slide Deck NEW & GitHub Examples NEW
- Algorithmic Differentiation Routines
- Derivative-free Optimization for Data Fitting
- Struve Functions
- NAG Optimization Modelling Suite
- Interior Point Method for Large Scale Linear Programming
- Interior Point Method for Nonlinear Optimization
- Semidefinite Programming (SDP)
- Three Body Problem using High-Order Runge–Kutta Interpolation
- Mixed Integer Nonlinear Programming
- Unscented Kalman Filter
- LARS / LASSO / Forward Stagewise Regression
- Change Point Analysis
- Confluent Hypergeometric Function
- Two-stage Spline Approximation to Scattered Data
- Multi-start Optimization
- Optimization for Non-negative Least Squares
- Matrix Functions
- Inhomogeneous Time Series
- Gaussian Mixture Model
- Best subset
- Bound Optimization BY Quadratic Approximation
- Linear Quantile Regression
- Sampling with Unequal Weights
- Calling random number generators from a multi-threaded environment
- Skipping Ahead the Mersenne Twister Random Number Generator
- Global Optimization
- Partial Least Squares / Ridge Regression
- Search routines
The Users' Notes for the NAG Library for Java are available here.
There is no Java-specific documentation. You should use the NAG Library documentation for information relating to a specific routine and its arguments. The NAG Library Manual, Mark 27 is available in the following formats:
The Library is organized into Chapters – each being documented with its own Introduction and Contents list followed by a comprehensive document for each function detailing its purpose, description, list of parameters and possible error exits. Example programs and results are also supplied. All examples are available online to facilitate their use as templates for the users' calling programs.
Produced by experts for use in a variety of applications, the NAG Library is the largest commercially available collection of numerical and statistical algorithms in the world. With over 1,600 tried and tested routines that are both flexible and portable it remains at the core of thousands of programs and applications spanning the globe. The NAG Library is widely used and trusted because of its unrivalled quality, reliability and portability. Whether it is a single PC or a cluster of the world’s largest supercomputers, the NAG Library has the numerical capabilities to fit your model. The NAG Library is available for use with many programming languages and for many platforms and operating systems.
Services and Support
NAG’s Technical Support Service is provided by a team of specialists in numerical and statistical software development, in fact the NAG Library and Compiler development team share responsibility for the support of our software. We strongly believe that in order to effectively support complex software the technicians must be both experienced and understand the intricacies of the computational techniques. This conviction is reflected in the composition of the team, most of whom are qualified to PhD level, and have combined experience of software support in excess of 50 years.
NAG provides accurate, documented, numerical software and services to help you make sure that your results are accurate. The validity of each NAG routine is tested for each platform that it is enabled for. Only when an implementation satisfies our stringent accuracy standards is it released. As a result, you can rely on the proven accuracy and reliability of NAG to give you the right answers. NAG is an ISO 9001 certified organization.
The numerical codes that underpin the results from your software are not future proof. While the mathematics does not change, the codes have a limited lifespan because of new hardware structures, mathematical innovation and changes in application needs. NAG Numerical Services help you and your organization find and implement the optimum numerical computation solutions. NAG works with your team to impart skills and techniques that will help solve your numerical software problems.
Your users, developers and managers can all benefit from NAG's highly regarded training courses. All of the training courses listed have been delivered successfully either from NAG offices or at client premises. Training courses can be tailored to suit your particular requirements and be targeted to novice, intermediate or experienced levels. Specialized mentoring and development programs are also available for HPC managers.
NAG was founded on collaboration as an inter-University collaborative venture combining the talents of mathematicians and computer scientists. NAG has continued to collaborate with individuals and organizations over the past four decades and today longstanding and new partners are delivering tangible benefits to users and students all over the world.