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.

I feel software/CS people largely avoid (or don’t need) certain areas of math.

To me, convex optimization is more the domain of engineering when there are continuous functions and/or stochastic processes involved.

Much of signal processing and digital communication systems are founded around convex optimization because it’s actually a sensible way to concretely answer “was this designed right?”.

One can use basic logic to prove a geometry proof, or the behavior of a distributed algorithm.

But if one wants to prove that a digital filter was designed properly for random/variable inputs, it leads to finding solutions of convex optimization problems (minimization of mean squared error or such).

Of course, whether the right problem is being solved is a different issue. MMSE is just mathematically extremely convenient but not necessarily the most meaningful characterization of behavior.

I’ve always thought it was weird too, and have spent far too much time thinking why - my best guess is that it’s used in Economics while other methods aren’t used outside programming curiosities (unless you need to apply it at work)

CP sits in a weird place between LP and general global optimization solvers. You need to study the solution space to make sure it is not higher order than CP can handle. And if your analysis fails, you may end up with local optimization. It is much easier to figure out if the problem is linear. So might as well switch to GA instead of risking it with CP.