  The column-and-constraint generation (CCG) method was introduced by
\citet{Zeng2013} for solving two-stage adaptive optimization. We found that the
CCG method is quite scalable, but sometimes, and in some applications often,
produces infeasible first-stage solutions, even though the problem is feasible.
In this research, we extend the CCG method in a way that (a) maintains
scalability and (b) always produces feasible first-stage decisions if they
exist. We compare our method to several recently proposed methods and find that
it reaches high accuracies faster and solves significantly larger problems.
