Wow, very cool. I learned something new today... Miles, in autodiff.jl<https://github.com/mlubin/NLsolve.jl/blob/4340c378ebb7b3864c88c1590f4385c677b37893/src/autodiff.jl>, are the function arguments all modified in place just to save memory in the case of large vectors, or is there some other reason?
On Tue, Feb 11, 2014 at 9:34 PM, Miles Lubin <[email protected]> wrote: > We're still in the process of putting together a nice interface for this, > but automatic differentiation is a good option that isn't available in most > other languages. It will give you an *exact* numerical derivative, not > subject to approximation error from finite differences. As an example of > how to use the DualNumbers package to compute a Jacobian matrix, see > https://github.com/EconForge/NLsolve.jl/pull/6. If you have any questions > on this, I'm happy to help out. > > As a fallback, the Calculus package has routines for computing a Jacobian > using finite differences. > > > On Tuesday, February 11, 2014 5:25:20 PM UTC-5, Mauro wrote: > >> You could try automatic differentiation. Have a look at the example in >> the readme of https://github.com/scidom/DualNumbers.jl >> >> On Tue, 2014-02-11 at 21:35, [email protected] wrote: >> > I imagine this exists somewhere already, but I haven't been able to >> find >> > it: is there a function that takes a vector-valued function and a point >> in >> > its domain, and returns the Jacobian matrix at that point? >> > >> > Thanks~ >> > >> > Sam >> >
