NAG Library for Python
NAG Library for Python
If you need to add mathematical and statistical functionality to your applications or if you have complex mathematical problems to solve, the NAG Library for Python will provide a host of benefits. The NAG Library for Python 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.
DemandTec was particularly attracted to NAG's Mixed Effect Regression, Correlation and Regression Analysis, Multivariate Methods and Time Series Analysis routines. Equally important for DemandTec was NAG's staff of algorithmic experts and software engineers who were not only available to help the DemandTec team with integration issues but were also capable of creating entirely new methods for incorporation into DemandTec's code. 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) Technical Poster NEW & GitHub Examples NEW
- Derivative-free Optimization Solver for Calibration Problems NEW
- Nearest Correlation Matrix Technical Poster NEW, GitHub Examples NEW & Mini Article
- 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 NAG Library for Python is the largest and most comprehensive collection of mathematical and statistical algorithms for Python available commercially today. Organizations all over the world rely on the NAG Library routines because of the quality and accuracy the software gives to their work. The NAG Library for Python has been developed to work quickly and seamlessly with Python and features many usability enhancements. All routines in the new package work effortlessly with NumPy data types.
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 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.