NAG Library - Latest Content
The NAG Library contains hundreds of routines which are powerful, reliable, flexible and ready for use from a wide range of operating systems, languages, environments and packages including Excel, Java, MATLAB®, .NET/C# and many more. We have selected key highlights from the NAG Library and show in more detail how a particular routine or set of routines can be used and explain how they complement the existing capabilities of the NAG Library. A full comprehensive overview of what is new can be found in the NAG Library Manual.
To learn more about a specific area/routine click on the relevant link below.
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
- Copulas
- Skipping Ahead the Mersenne Twister Random Number Generator
- Global Optimization
- Partial Least Squares / Ridge Regression
- Quantiles
- Search routines