On 9/27/25 10:27, Sean Carney wrote:

In implementing this finite difference check in deal.II, however, I only observe a decrease in the error between (*) and (**) up to \epsilon \approx 10^{-3}. After, that, the error increases. The details can be seen the attached screenshot.

For reference, I am using a (sparse) direct solver for solving both the state and adjoint equations. The behavior is essentially the same if a CG solver is used instead. I am using Q2 finite elements, but I observe the same behavior for Q3 as well.

Additionally, it is worth mentioning that in a sanity-check-test where the objective function is modified to simply be:
      J(z) = \alpha/2 \| z\|_2^2
so that the derivative is simply
      J'(z) = \alpha z
(i.e. no adjoint calculation needed), the convergence behavior */is/* what one would expect (i.e. the error decreases up to \epsilon ~ 1e-7). So, the issue should be with the adjoint solve.

*Can anyone point out a reason why I /shouldn't /expect the standard behavior?* Or, perhaps there is an error in my adjoint calculation. I tried to produce a light-weight, minimal-working-example. It is attached here.

Sean,
I don't see a particular reason why you shouldn't get the expected convergence, but have also not spent as much time as you thinking this through. Does the discretization error not play a role in this? Presuambly you are comparing the FD gradient against an analytical one? Or against the gradient computed via the dual solution?

My suggestion would be to make the problem simple. For example, let z use piecewise constants and u,p use piecewise quadratics. Then if you let u_D be a constant (perhaps zero), and if you work in 1d, the finite element approximation gives you the exact solution. In those cases, you can probably compute the exact solution of the problem on a piece of paper, along with what the exact gradient is. You should be able to compare both the adjoint-based gradient and the FD gradient against that.

So these would be my steps to debug the issue -- please report back what you find!

Best
 W.

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