I didn't miss the point. my graduate work was using constraint optimizers in molecular dynamics (n-body physics) and it translated to ML (I didn't have to relearn anything). The one part that was truly simpler is the objective functions in ML are believable to be convex, while n-body physics with constraints are highly non-convex).