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] <javascript:> 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 >
