I think by react you meant react to, which was fair enough, it was dodgy as. Now running well.

The property of getting a guaranteed optimum is something that arises from setting the design problem up as a geometric program. You won't always get a solution (for example, a certain size quad has a minimum prop diameter). Though if you do, it is guaranteed to be optimum because the problem is convex as outlined here: https://gpkit.readthedocs.io/en/latest/gp101.html

Also, conflicting objectives are naturally traded off until you find a Pareto optimal solution. Using geometric programs lets you balance conflicts in a design quickly. This is why they are super useful for aerospace applications, where you are always looking at a tradeoff between weight and at least one other variable.

I meant to write reach, sorry. Glad that it's working well now. Thanks for the reply, and for the link, it sounds like something fun to get into =).

It forces you to assign a weight to each objective, converting from a multi-objective to a single-objective optimization problem (the weighted sum method).

By varying the weights and re-running, you could identify a set a solutions on [convex regions of] the Pareto front.

This is correct, much better than what I said.