Closed Akshay5312 closed 1 year ago
Just FYI, it should be possible to generalise this to differentiating functions of $R^n \rightarrow R^m$ such that we can twice differentiate functions to get a hessian. See here for an example (this doesn't exploit the symmetry of the hessian unfortunately but :man_shrugging: )
Done with #58
A way to numerically approximate the jacobian given a vector valued function, (or the gradient of an Rn -> R function) would be beneficial