Description
Automatic differentiation becomes easier to add to C++ numerical code. It is useful for developers working on optimization, machine learning, scientific computing, or sensitivity analysis.
Numerical libraries can produce wrong results if types, precision, or assumptions are misused. Validate gradients against finite differences or known cases.