How do interpret the scaling report to improve my model?
Steve Y
Pretty nice feature. I am trying to figure out how to interpret the report to see if there are any glaring problems in my model that includes a trajectory with Dymos. My model usually converges fine, although sometimes I have to change the NLP scaling to gradient based. I have no idea what that really does but usually it will make IPOPT converge if the default setting doesn't work, and vice versa.
This the what the Jacobian looks like according to the tool
I am guessing that what is desired is that the order of magnitude of the partials in the Jacobian spans as few orders of magnitude as possible. The lower two diagonal bands have magnitudes from 0.1 to 10E5 and seem to be related to phase linkages. For example we have 'traj.linkages.phase_1:h_final|phase_2:h_initial wrt traj.phases.phase_1.indep_states.states:h' with a magnitude of 10E5. Should I be doing something about this? 
In the design variables everything seems to be scaled ok, with driver values an order of magnitude of 1. In the constraints report the OOM span is wider from 10E-5 to 10E2. I am not setting defect_refs. Maybe I need to do something here?
Rob Falck
I tend to use the table that shows the norms from the driver and model perspectives.
In this case the driver values are all scaled on the order of 1 or so. If they're huge, then the scalers/refs may need to be adjusted. (Scaling to the order of around 1 isn't guaranteed to be a good strategy, but it's usually a pretty good going in position.
You may want to do this after limiting the driver iterations. For a converged case in dymos, the values of the defect constraints are going to be close to zero and this may not give you a good idea of their initial values.
For defect_refs, for instance, if I note that the value of the constraint were something like 5.5E4, then I might set that defect ref to 1.0E-4. Again, unit scaling isn't always correct, but it does frequently work.
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