An augmented Lagrangian approach to non-convex SAO using diagonal quadratic approximations

Successful gradient-based sequential approximate optimization (SAO) algorithms in simulation-based optimization typically use convex separable approximations. Convex approximations may however not be very efficient if the true objective function and/or the constraints are concave. Using diagonal quadratic approximations, we show that non-convex approximations may indeed require significantly fewer iterations than their convex counterparts. The nonconvex subproblems are solved using an augmented Lagragian (AL) strategy, rather than the Falk-dual, which is the norm in SAO based on convex subproblems