Module forward 

Forward mode automatic differentiation

Forward mode AD is efficient for computing gradients when the number of inputs is small compared to the number of outputs.

Structs

ForwardAD

Forward mode automatic differentiation engine

ForwardODEJacobian

Forward mode AD for ODE right-hand side functions

VectorizedForwardAD

Vectorized forward mode AD for computing multiple directional derivatives

Functions

example_rosenbrock_gradient

Example: Rosenbrock function gradient

forward_gradient

Compute gradient using forward mode AD (convenience function)

forward_jacobian

Compute Jacobian using forward mode AD (convenience function)