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Optimization

Optimization theory is a building block of operations research and data science. It combines disciplines of algebra, geometry, analysis, combinatorics, probability, statistics, and computer science for data-driven decision-making in complex systems. Our methodological research in optimization spans theory, analysis, and design of computationally efficient, robust, and scalable algorithms to handle real-world problems in engineering, operations, economics, and business. 

This Area Includes:

  • Discrete and Combinatorial Optimization
  • Stochastic and Robust Optimization
  • Network Optimization
  • Approximate Dynamic Programming
  • Markov Decision Process
  • Approximation Algorithms
  • Game Theory
  • Reinforcement Learning

Faculty in this Area: 

Tugce IsikAmin KhademiMary Elizabeth KurzQi LuoHamed RahimianThomas SharkeyYongjia Song, Kevin M. TaaffeEmily Tucker

 

 

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