I am using the finite difference scheme to find gradients. Lets say i have 2 outputs (y1,y2) and 1 input (x) in a single component. And in advance I know that the sensitivity of y1 with respect to x is not same as the sensitivity of y2 to x. And thus i could potentially have two different steps for those as in ;
self.declare_partials(of=y1, wrt=x, method='fd',step=0.01, form='central')
self.declare_partials(of=y2, wrt=x, method='fd',step=0.05, form='central')
There is nothing that stops me (algorithmically) but it is not clear what would openmdao gradient calculation exactly do in this case?
does it exchange information from the case where the steps are different by looking at the steps ratios or simply treating them independently and therefore doubling computational time ?
I just tested this, and it does the finite difference twice with the two different step sizes, and only saves the requested outputs for each step. I don't think we could do anything with the ratios as you suggested, as the reason for using different stepsizes to resolve individual outputs is because you don't trust the accuracy of the outputs at the smaller (or large) stepsize.