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.
Methodologies |
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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 |
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Energy Systems |
Logistics, Production, and Scheduling |
Statistical Learning |
Water Resource Management |