Optimization

Optimization is a scientific approach to decision-making for complex systems. It utilizes mathematical models that are abstract representations of the actual system. Due to its versatility and wide ranging impact, optimization serves as a keystone in operations research and analytics.

Our research spans theory, the design of algorithms, computational innovation, and modeling for a wide range of applications. We seek to develop flexible, robust, and scalable techniques that can handle complex mathematical structures arising from real-world problems.

optimization problem illustration
plot of 3 axis optimization
Methodologies
Complementarity and Equilibrium Constraints
Convex Optimization
Discrete and Combinatorial Optimization
Dynamic Optimization
Large-Scale Optimization
Network Optimization
Nonlinear Optimization
Optimization Under Uncertainty: Data-Driven Optimization, Markov Decision Processes, Robust Optimization, Stochastic Optimization, Simulation Optimization
Applications
Energy Systems
Logistics, Production, and Scheduling
Statistical Learning
Water Resource Management

 

Concentration Faculty

Affiliated Faculty