Equality constraints means you're working in a lower-dimensional subspace, I'd have to find out more about your algorithms but I'd be surprised if you wound up evaluating many feasible points at all (esp. with nonlinear equalities).
L2 penalty would likely converge to an infeasible solution. Augmented Lagrangian would be better, but then you're making users handle dual updates. At that point I'd rather use an actual constrained optimization library that does the algorithm carefully, and use primal-dual interior point. Not having this kind of thing built in counts as "no constrained optimization" IMO.
L2 penalty would likely converge to an infeasible solution. Augmented Lagrangian would be better, but then you're making users handle dual updates. At that point I'd rather use an actual constrained optimization library that does the algorithm carefully, and use primal-dual interior point. Not having this kind of thing built in counts as "no constrained optimization" IMO.