optim_doc: jacobs

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### 4.6 Complex step derivatives

Function File: Df = jacobs (x, f)
Function File: Df = jacobs (x, f, hook)

  Calculate the jacobian of a function using the complex step method.

  Let f be a user-supplied function. Given a point x at which we seek for the Jacobian, the function `jacobs` returns the Jacobian matrix `d(f(1), …, df(end))/d(x(1), …, x(n))`. The function uses the complex step method and thus can be applied to real analytic functions.

  The optional argument hook is a structure with additional options. hook can have the following fields:

  - `h` - can be used to define the magnitude of the complex step and defaults to 1e-20; steps larger than 1e-3 are not allowed.
  - `fixed` - is a logical vector internally usable by some optimization functions; it indicates for which elements of x no gradient should be computed, but zero should be returned.

  For example:

      f = @(x) [x(1)^2 + x(2); x(2)*exp(x(1))];
      Df = jacobs ([1, 2], f)