# Examples¶

To quickly get an idea how using XAD looks like in practice, see the examples below. All code snippets require that the function to be differentiated is callable with the active data type for algorithmic differentiation.

Note that XAD ships with a set of examples that can be used as a starting point for development. Further, for examples how to use it with a large-scale project, please see QuantLib Integration.

## First Order Forward Mode¶

```
// types for first-order forward mode in double precision
using mode = xad::fwd<double>;
using Adouble = mode::active_type;
// independent variables
Adouble x0 = 1.0, x1 = 1.5, x2 = 1.3, x3 = 1.2;
derivative(x0) = 1.0; // seed directional derivative
// (calculate dy/dx0)
Adouble y = func(x0, x1, x2, x3);
std::cout << "y = " << value(y) << "\n"
<< "dy/dx0 = " << derivative(y) << "\n";
```

## First Order Adjoint Mode¶

```
// types for first-order adjoints in double precision
using mode = xad::adj<double>;
using Adouble = mode::active_type;
using Tape = mode::tape_type;
Tape tape;
// independent variables and start taping
std::vector<Adouble> x ={1.0, 1.5, 1.3, 1.2};
tape.registerInputs(x);
tape.newRecording();
Adouble y = func(x[0], x[1], x[2], x[3]);
tape.registerOutput(y);
derivative(y) = 1.0; // seed output adjoint
tape.computeAdjoints(); // roll-back tape
std::cout << "y = " << value(y) << "\n"
<< "dy/dx0 = " << derivative(x[0]) << "\n"
<< "dy/dx1 = " << derivative(x[1]) << "\n"
<< "dy/dx2 = " << derivative(x[2]) << "\n"
<< "dy/dx3 = " << derivative(x[3]) << "\n";
```

## Second Order Forward over Adjoint Mode¶

```
// types for second-order foward-over-adjoint in double
using mode = xad::fwd_adj<double>;
using Adouble = mode::active_type;
using Tape = mode::tape_type;
Tape tape;
// independent variables
std::vector<Adouble> x = {1.0, 1.5, 1.3, 1.2};
// seed directional derivative for 2nd order forward
derivative(value(x0)) = 1.0;
// register inputs on tape and record function calls
tape.registerInputs(x);
tape.newRecording();
Adouble y = func(x0, x1, x2, x3);
value(derivative(y)) = 1.0; // seed 1st order adjoint
tape.computeAdjoints(); // roll-back tape
std::cout << "y = " << value(value(y)) << "\n"
<< "\nfirst order derivatives:\n"
<< "dy/dx0 = " << value(derivative(x[0])) << "\n"
<< "dy/dx1 = " << value(derivative(x[1])) << "\n"
<< "dy/dx2 = " << value(derivative(x[2])) << "\n"
<< "dy/dx3 = " << value(derivative(x[3])) << "\n"
<< "\nsecond order derivatives w.r.t. x0:\n"
<< "d2y/dx0dx0 = " << derivative(derivative(x[0])) << "\n"
<< "d2y/dx0dx1 = " << derivative(derivative(x[1])) << "\n"
<< "d2y/dx0dx2 = " << derivative(derivative(x[2])) << "\n"
<< "d2y/dx0dx3 = " << derivative(derivative(x[3])) << "\n";
```