Hessian automatic differentiation

Hessian automatic differentiation (Hessian AD) is a specialized form of automatic differentiation (AD) that focuses on computing second-order derivatives, specifically the Hessian matrix of a scalar-valued function with respect to its input variables. The Hessian matrix is a square matrix of second-order partial derivatives and is essential in optimization, particularly when analyzing the curvature of a function or when applying certain optimization algorithms that leverage second-order information.