I posted this question of the `autodiff`

Slack channel but haven’t heard back.

I’m trying to compute Hessian-vector products in place with ReverseDiff. I tried the following

```
julia> function arglina(x)
n = length(x)
m = 2 * n
return sum((x[i] - 2/m * sum(x[j] for j = 1:n) - 1)^2 for i = 1:n) + sum((-2/m * sum(x[j] for j = 1:n) - 1)^2 for i = n+1:m)
end
arglina (generic function with 1 method)
julia> ∇f!(g, x) = ReverseDiff.gradient!(g, arglina, x);
julia> ∇²fv!(x, v, hv) = begin
g = similar(x)
ReverseDiff.gradient!(hv, x -> dot(∇f!(g, x), v), x)
end
∇²fv! (generic function with 1 method)
julia> x = ones(5); v = copy(x); hv = similar(x);
julia> ∇²fv!(x, v, hv)
5-element Array{Float64,1}:
0.0
0.0
0.0
0.0
0.0
```

Allocating new vectors for the result works:

```
julia> ∇f(x) = ReverseDiff.gradient(arglina, x);
julia> ∇²fv(x, v) = ReverseDiff.gradient(x -> dot(∇f(x), v), x);
julia> ∇²fv(x, v)
5-element Array{Float64,1}:
2.0
2.0
2.0
2.0
2.0
```

Am I doing something wrong here? Or is this Perturbation Confusion (Nested Differentiation Bug) · Issue #83 · JuliaDiff/ForwardDiff.jl · GitHub?

Thanks!