NAG Library Latest Content
What's new?
We have selected the new functionality in the NAG Library and show in more detail how a particular routine or set of routines can be used:
Faster Data Fitting (Calibration): Mini Article, GitHub Examples
Fast Implied Volatilities: Mini Article
Solving Convex and Non-convex Quadratically Constrained Quadratic Programming (QCQP) Problems: Mini Article
Solving Convex Problems with Second Order Cone Programming (SOCP): Mini Article, Technical Poster & GitHub Examples
Derivative-free Optimization Solver for Calibration Problems: Technical Poster & Mini Article
First-order Active-Set Method for Nonlinear Programming: Mini Article, GitHub Examples
Nearest Correlation Matrix: Technical Poster, GitHub Examples & Mini Article
Randomized Numerical Linear Algebra (RNLA) Algorithms: Technical Poster
Non-negative Matrix Factorization for Analysing High-dimensional Dataset: Slide Deck & GitHub Examples
More NAG Library key functionality
- 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
"The code is highly reliable. The NAG Library contains some of the best algorithms available"
Fred Hickernell, Professor, Illinois Institute of Technology