Update on zygote and refreshing. (#756)

source/rst/more_julia/optimization_solver_packages.rst

@@ -289,9 +289,9 @@ with 1 million dimensions, solved in a few seconds.
 
 Caution: while Zygote is the most exciting reverse-mode AD implementation in Julia, it has many rough edges.
 
+- If you write a function, take its gradient, and then modify the function, you need to call ``Zygote.refresh()`` or else the gradient will be out of sync.  This may not apply for Julia 1.3+.
 - It provides no features for getting Jacobians, so you would have to ask for each row of the Jacobian separately.  That said, you
   probably want to use  ``ForwardDiff.jl`` for Jacobians if the dimension of the output is similar to the dimension of the input.
-- If you use ``gradient(f, x)`` for some ``f``, then change the function ``f`` and rerun ``gradient(f, x)``, it may not update
 - You cannot, in the current release, use mutating functions (e.g. modify a value in an array/etc.) although that feature is in progress
 - Compiling can be very slow for complicated functions.
 
@@ -688,4 +688,4 @@ With that, we can seed a dual number and find simple derivatives,
 For this assignment:
 
 1.  Add in AD rules for the other operations: ``*, -, ^, exp``.
-2.  Come up with some examples of univariate and multivariate functions combining those operations and use your AD implementation to find the derivatives.
+2.  Come up with some examples of univariate and multivariate functions combining those operations and use your AD implementation to find the derivatives.