[krisoft]

I’m a software engineer who has been trained on convex programming. I have read the Boyd book, did some hobby projects in the area. But it is just not comming up during the day to day work. Even when i have a task well suited for continous value optimisation it does never seem to be a good fit for convex programming. The application areas were sensor calibration, slam, model predictive control, trajectory prediction and the like. Usually when this happens we just throw the problem at ceres solver and deal with it when it is not converging. Would be nice to have the strong guarantees a convex optimiser could give us but I’m not finding a way in practice.

It is probably just a “git gud” situation. I even re-read Lars Blackmore’s “Lossless Convexification of Nonconvex Control Bound and Pointing Constraints of the Soft Landing Optimal Control Problem” from time to time hoping that i find a way to apply a similar convexification idea to my problems. With all of that I’m not that surprised that convex optimisation is not more widely known.