# No method matching terminate!(::DiffEqSensitivity.FakeIntegrator

**URL:** <https://discourse.julialang.org/t/no-method-matching-terminate-diffeqsensitivity-fakeintegrator/66159>\
**Category:** General Usage\
**Tags:** flux, sciml\
**Created:** [August 10, 2021, 11:40pm UTC](https://discourse.julialang.org/t/no-method-matching-terminate-diffeqsensitivity-fakeintegrator/66159 "2021-08-10T23:40:25Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![iqopi](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/iqopi/32/25101_2.png) [@iqopi](https://discourse.julialang.org/u/iqopi)\
**Post date:** [August 10, 2021, 11:40pm UTC](https://discourse.julialang.org/t/no-method-matching-terminate-diffeqsensitivity-fakeintegrator/66159/1 "2021-08-10T23:40:25Z")

</div>

Hey,

i am trying to optimize the parameters of a Differential equation using the DiffEqFlux.  
Since i am only interested into the new steady state, i am using a callback to terminate the integration, once this new state is reached. This looks sth like:

```julia
condition(u,t,integrator) = sum( abs.(u[2*n+1:end]) ) < 1e-7*sum(abs.(barX))
affect!(integrator) = terminate!(integrator)
cb = DiscreteCallback(condition,affect!)
sol = solve(prob,BS3(),p=p,callback=cb)

```

However, if i hit this callback when calculating the gradient, i get the following error message:

```julia
ERROR: MethodError: no method matching terminate!(::DiffEqSensitivity.FakeIntegrator{Vector{Float64}, Vector{Float64}, Float64})

```

So it seems, the `terminate!` is not implemented yet in the `DiffEqSensitivity` stack.  
Is there an easy way to circumvent this problem?

(sorry for not providing a complete working example. hope its still clear, what the problem is.)

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [August 11, 2021, 1:12am UTC](https://discourse.julialang.org/t/no-method-matching-terminate-diffeqsensitivity-fakeintegrator/66159/2 "2021-08-11T01:12:43Z")

</div>

> [@iqopi](#):
>
> Is there an easy way to circumvent this problem?

Yes, a pure AD sensealg should be fine. `sol = solve(prob,BS3(),p=p,callback=cb,sensealg=ReverseDiffAdjoint())`. But we should get an issue open on this anyways. However…

> [@iqopi](#):
>
> Since i am only interested into the new steady state, i am using a callback to terminate the integration, once this new state is reached.

In this case you don’t need to differentiate through the ODE system at all! If you use `SteadyStateProblem`, it has a special adjoint that treats the case nicely.

---

<div class="post-metadata">

**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [August 11, 2021, 1:59am UTC](https://discourse.julialang.org/t/no-method-matching-terminate-diffeqsensitivity-fakeintegrator/66159/3 "2021-08-11T01:59:14Z")

</div>

And actually I was just reviewing. [https://github.com/SciML/DiffEqSensitivity.jl/pull/445](https://github.com/SciML/DiffEqSensitivity.jl/pull/445) should solve this.

---

<div class="post-metadata">

**Author:** ![iqopi](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/iqopi/32/25101_2.png) [@iqopi](https://discourse.julialang.org/u/iqopi)\
**Post date:** [August 11, 2021, 9:16am UTC](https://discourse.julialang.org/t/no-method-matching-terminate-diffeqsensitivity-fakeintegrator/66159/4 "2021-08-11T09:16:46Z")

</div>

Thanks so much, however, it i do that, i get a different error:

```julia
#%%
using DifferentialEquations, DiffEqFlux, Plots, Flux

#%%
function f!(du, u, p, t)
  du[1] = - p[1]*u[3]
  du[2] = - p[2]*u[3]
  du[3] = - du[1]*u[2] - du[2]*u[1]
end

#%%
u0 = [1.,1.,1.]

tspan = (0.0, 10.0)

#%%
condition(u,t,integrator) = abs.(u)[3] < 1e-4
affect!(integrator) = terminate!(integrator)
cb = DiscreteCallback(condition,affect!)

#%%
p = [1.,.5]
prob = ODEProblem(f!, u0, tspan,p)

sol = solve(prob,BS3(),p=p,callback = cb)
fit_sol = sol.u[end]

#%%
# Setup the ODE problem, then solve
plot(sol)

#%%
function loss(pl)
    sol = solve(prob,BS3(),p=pl) # ,callback=cb,sensealg=ReverseDiffAdjoint())
    state = sol.u[end]
    return sum(abs,sol[end][1:2] - fit_sol[1:2])
end

#%%
loss(p)

#########################
# THIS WORKS
#########################
#%%
pv = rand(2)
par = Flux.params(pv)
grads = gradient(() -> loss(pv), par )
grads[pv]

#%%
function loss(pl)
  sol = solve(prob,BS3(),p=pl,callback=cb,sensealg=ReverseDiffAdjoint())
  state = sol.u[end]
  return sum(abs,sol[end][1:2] - fit_sol[1:2])
end

#%%
loss(p)

#####################
# Here i get an error
#####################
#%%
par = Flux.params(pv)
grads = gradient(() -> loss(pv), par )
grads[pv]

```

The error is

```julia
ERROR: LoadError: TrackedArrays do not support setindex!
Stacktrace:

```

and it points back to the `sol=...` in the loss function