NAG Decision Tree

Figure 1: Decision Tree

NAG Decision Tree

With the Decision Tree, researchers in a wide range of disciplines can solve difficult optimization problems using easy to follow step-by-step instructions on how to select the best-suited local or global optimization routines.

The trees are especially useful in helping both new and experienced users to select the appropriate routine for the problem at hand in a matter of minutes.
It is part of the documentation for one of the most rigorously tested and documented sets of optimization routines and other mathematical and statistical algorithms in the world, collected into the Numerical Algorithms Group Library.

The library of routines, including the optimization chapters, can be called from diverse environments such as C++, Fortran, MATLAB and R.