If you have access to the SBB solver you may try this. It uses branch and bound directly on the nonlinear model where DICOPT branches on a linear approximation. If the linear approximation is not a very good approximation to the overall model, DICOPT will not predict the binary variables very well and you may get the infeasible NLPs you experience. DICOPT just cuts away this one binary point and tries again which is not very effective when NLPs are infeasible. If SBB experiences an infeasible subproblem then it can prune a whole branch of the B&B tree, so it loves infeasible NLPs.
Alternatively, you can
- Try to solve it with a different NLP subsolver, e.g. CONOPT instead of MINOS
- Try to solve it with a different MINLP solver, e.g. SBB, BONMIN or one of the global codes.