Hi Robert,
I was reading your document “Grid Interpolation Layer” and I was wondering if you could add the pseudocode for the case with decision variables, preferably in infinite horizon.
I noticed that your verbal explanation does not mention refinmement, maybe because in the previous section you explained the finite horizon case, where refinement is not needed.
As far as I understand, with refinement we precompute F(d,a',a,z) and then maximize with respect to d to obtain F^*(a',a,z) and d^*(a',a,z). But we need the optimal d for all possible points on the fine grid over a'. This means that F will be a huge matrix. Even when using lowmemory, where we loop over z, we still have to store F(d,a’,a) which is big.
Thanks!
I am also trying to understand the memory requirements of my model. I have the following grid sizes:
n_d = 50
n_a = [750, 3]
n_z = 50
If I use interpolation, I can decrease n_a(1) to 300 points or similar, I guess.