Theory and methodology for nonlinear optimization
- Semidefinite programming and conic programming
- Integer programming
- Primal-dual methods for large-scale convex optimization
- Self-concordant convex optimization
- Numerical methods for nonlinear optimization such as sequential convex programming and Gauss-Newton methods.
- Optimality and constant rank constraint qualification in optimization.
Variational inequalities and equilibrium problems
- Optimization under uncertainty; stochastic optimization and equilibria
- Sensitivity analysis for variational inequalities and equilibrium problems
- confidence regions and intervals for stochastic variational inequalities.
- Sensitivity analysis of traffic user equilibria
- Inference for sparse penalized statistical regression
Applications of Optimization
- Optimal control, linear and nonlinear model predictive control (MPC)
- Linear feedback controller design
- Machine learning and statistics
- Image and signal processing, and compressive sensing
- Operations Research