Efficient Vector^T * Jacobian matrix?

Specific Domains Numerics
3

I think you can use something like

function vjp(f::Function, x::AbstractVector, v::AbstractVector)
    g(y) = dot(v, f(y))
    ForwardDiff.gradient(g, x)
end

Though reverse mode autodiff would be much more efficient unless your vector x is short.

[Edit: Although it looks nice, I think this formulation amounts to computing the full Jacobian internally (possibly in batches when x is long… but nevertheless).]

1 Like