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Computational Optimization

Research in computational optimization includes large-scale discrete and continuous optimization problems, focusing on optimization under uncertainty, robust optimization, and interior point methods. Research includes fundamental methodological investigations as well as applications in manufacturing, healthcare systems, workforce planning, terminal operations, weather prediction, and financial engineering. Recent investigations include the development of algebraic modeling software for mathematical programming, kernel methods in machine learning, network algorithms, and the design of mathematical decomposition algorithms for solving large-scale decision problems.

Centers and Laboratories

The Laboratory of Optimization and Intelligent Systems (LOIS) supports basic research in optimization, intelligent systems and automated learning methods, including predictive data mining, statistical methodology, machine learning and knowledge discovery, and associated solution algorithms.

Recent Theses and Dissertations

"A comparison of Dynamic and Integer Programming Algorithms for a Bidder Selection Problem," advised by Hillel Kumin

"A Mean-Variance Model for Stochastic Time-Dependent Networks," advised by Simin Pulat

"Incremental Kernel Learning Algorithms and Applications," advised by Theodore Trafalis

"Least-Squares Multi-Class Kernel Machines with Prior Knowledge and Applications," advised by Theodore Trafalis

"Uncertainty and Sensitivity Analysis in Support Vector Machines: Robust Optimization and Uncertain Programming Approaches," advised by Theodore Trafalis

"Unconstrained Learning Machines," advised by Theodore Trafalis

Associated Faculty