ForwardDiff.jl : ERROR: LoadError: MethodError: convert(...) is ambiguous

[F-YF]

@mcabbott

This looks fine to me, for now. But CR and ForwardDiff don’t depend on each other, so some other solution ought to be invented longer-term.

@contradict

This definition seems to work, but I am not confident it is correct:

ForwardDiff.jl: ERROR: LoadError: MethodError: no method matching Float64(::ForwardDiff.Dual{ForwardDiff.Tag{Zygote.var"#104#105"{var"#f#1"}, Float64}, Float64, 1})

I had a lot of problems with ForwardDiff. I used Zygote to do automatic differential calculations before, so I didn’t know much about ForwardDiff. My original intention was to use Zygote to achieve the gradient of gradients, but there were a lot of problems with twice automatic differentiation in reverse mode. So I used Zygote.hessian to achieve the mixing forward + reverse mode, and then many problems occurred. I have solved some problems by myself, and some problems have been solved by useful…

This is a new problem I encountered with forwarddiff. I wonder if you are interested in it