Advances in high-performance computing have led to a new generation of complex optimization problems that span the breadth of nonlinear optimization--from problems where discrete decisions must be made, to problems with constraints arising from simulation evaluations, to those governed or modeled by variational inequalities.

This project centers on a unified approach for addressing this diverse set of problems. We devise methods that fully exploit the structures arising within and between these classes of nonlinear optimization problems.