ad32rheolef

Automatic differentiation with respect to variables in r^3

Overview

The ad3 class defines a forward automatic differentiation in R^3 for computing automatically the gradient of a function from R^3 to R. The implementation uses a simple forward automatic differentiation method.

Examples

    template<class T>
    T f (const point_basic<T>& x) { return sqr(x[0]) + x[0]*x[1] + 1/x[2]; }
    ...
    point_basic<ad3> x_ad = ad3::point (x);
    ad3 y_ad = f(x_ad);
    cout << "f       ="<<y_ad <<endl
         << "gradq(f)="<<y_ad.grad() <<endl;