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

  • 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

 


Theodore Allen   |   Güzin Bayraksan   |   Chen Chen   |   Antonio J. Conejo   |   Sam Davanloo   |   Parinaz Naghizadeh   |   Marc Posner   |   Ramteen Sioshansi


 

Affiliated Faculty