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I have two examples in real use cases that can trigger perturbation confusion error in ForwardDiff.jl.
The first example (Lagrangian):
using ForwardDiff
m,g =1, 9.8
t =1
q = [1,2]
q̇ = [3,4]
L(t,q,q̇) = m/2*dot(q̇,q̇) - m*g*q[2]
∂L∂q̇(L, t, q, q̇) = ForwardDiff.gradient(a->L(t,q,a), q̇)
Dqq̇(L, t, q, q̇) = ForwardDiff.jacobian(a->∂L∂q̇(L,t,a,q̇), q)
Dqq̇(L, t, q, q̇) # ERROR: potential perturbation confusion...
The second one (Hamiltonian):
using ForwardDiff
m,g =1, 9.8
t =1
p = [5,6]
q = [1,2]
functionLegendre_transformation(F, w)
wv = a->ForwardDiff.gradient(F, a)
z =zeros(w)
M = ForwardDiff.jacobian(wv, z)
b =wv(z)
v =Symmetric(M)\(w-b)
w'v -F(v)
endfunctionLagrangian2Hamiltonian(Lagrangian, t, q, p)
L = q̇ ->Lagrangian(t, q, q̇)
Legendre_transformation(L, p)
endLagrangian2Hamiltonian(L, t, q, p) # This is OK
ForwardDiff.gradient(a->Lagrangian2Hamiltonian(L, t, a, p), q)
# ERROR: potential perturbation confusion...
I have two examples in real use cases that can trigger perturbation confusion error in ForwardDiff.jl.
The first example (Lagrangian):
The second one (Hamiltonian):
P.S.: I am on ForwardDiff master branch
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