Non-linear optimization is essential to many areas of geometry processing research. However, when experimenting with different problem formulations or prototyping new algorithms, a major practical obstacle the need figure out derivatives objective functions, especially second-order are required. Deriving and manually implementing gradients Hessians both time-consuming error-prone. Automatic dif...
