### Minimize difference for single point

Most examples I've found concerning dolfin adjoint minimizing a function go something like:

j = 0.5*float(dt)*assemble((u_0 - d)**2*dx)

But what if rather than minimizing the difference between an entire function and a function representing a set of experimental data, I just wanted to minimize the difference between a single data point and a point from the fenics function. Something like:

j = (Fenics_function_Point - experimental_data_point)

How would I go about setting a J Function like this, if possible?

I've seen a previous post (Is it possible to compute the gradient of a Functional only at a specific point with dolfin-adjoint?) that was close to what I was looking for, but not exactly.

If more details would be helpful, just let me know.

Thanks,

Adam