Automatic chain differential for system of equations?

[ovainola]

Hi,

I'm working on a problem, which has a system of equations. I would like to get partial derivates of the whole system.

julia> using ForwardDiff

julia> f1(s) = (s/3)^2
f1 (generic function with 1 method)

julia> f2(s, l) = s^2 + exp(3*l/sqrt(4))
f2 (generic function with 1 method)

julia> function f(x)
           return f1(x[1]) + f2(x[1], x[2])
       end
f (generic function with 1 method)

julia> g = forwarddiff_gradient(f, Float64)
g (generic function with 1 method)

julia> g([0.1, 56.2])
ERROR: BoundsError: attempt to access 1-element Array{DualNumbers.Dual{Float64},
1}:
 0.1+0.0du
  at index [2]
 in dual_fad at C:\Users\ovax03\.julia\v0.4\ForwardDiff\src\dual_fad\univariate_
range.jl:6
 in g at C:\Users\ovax03\.julia\v0.4\ForwardDiff\src\dual_fad\univariate_range.j
l:22

Is there an error in my code or should I make a macro, which would derivate all the terms?

[jrevels]

That does indeed look like a bug on our end, thanks for filing!

This package is undergoing a huge refactor at the moment, explained in this comment here. This bug will be fixed once #27 is merged (hopefully within the next 2 weeks). Here's some example code, working directly from the nduals-refactor branch:

julia> using ForwardDiff

julia> f1(s) = (s/3)^2
f1 (generic function with 1 method)

julia> f2(s, l) = s^2 + exp(3*l/sqrt(4))
f2 (generic function with 1 method)

julia> function f(x)
                  return f1(x[1]) + f2(x[1], x[2])
              end
f (generic function with 1 method)

julia> g = gradient_func(f, Partials{2,Float64}, mutates=false)
gradf (generic function with 1 method)

julia> g([0.1, 56.2])
2-element Array{Float64,1}:
 0.222222
 6.12514e36

In the meantime, you could try changing this line in the code you posted:

julia> g = forwarddiff_gradient(f, Float64)

to this:

julia> g = forwarddiff_gradient(f, Float64, fadtype=:typed, n=2)

and that should work as a temporary fix.

[jrevels]

This now fixed on master as of the merging of #27.

[ovainola]

👍