Using dual numbers and modern C++ you can write a library that can do this: auto lambda = [](auto x, auto y) { // The Rosenbroc

Using dual numbers and modern C++ you can write a library that can do this: auto lambda = [](auto x, auto y) { // The Rosenbrock function. auto d0 = y[0] - x[0]*x[0]; auto d1 = 1 - x[0]; return 100 * d0*d0 + d1*d1; }; // The lambda function will be copied and // automatically differentiated. The derivatives // are computed using templates, not numerically. // // No need to derive or compute derivatives // manually! auto func = make_differentiable<1, 1>(lambda); "func" now has code for first-order, second-order derivatives all generated and heavily optimized at compile-time. This is one reason C++ is so good for (mathematical) optimization.