You can get constrained optimization in general for unconstrained nonlinear problems (see the work N Sahinidis has done on BARON). The feasible sets are defined in the course of solving the problem and introducing branches. But that is both slow, doesn't scale to NN sizes, and doesn't really answer the question ML folks are asking (see the talk at the IAS on "Is Optimization the Right Language for ML").