- Optimization algorithms and
Faster and more effective algorithms and software for nonlinear,
mixed-integer, and linear programming.
- Feasibility and infeasibility
in optimization. Ways of reaching a feasible solution more quickly for nonlinear and
mixed-integer programs, and of analyzing infeasible optimization
models. Spin-off applications from algorithms for analyzing
infeasibility, including data compression.
- Optimization formulation
Automated tools for analyzing and debugging optimization models. For
example, one tool analyzes the shape of nonlinear functions and regions to
help select the correct solver.
- Applied optimization. Examples include transistor
sizing, DSP task-to-processor assignment, flexible manufacturing systems,
forestry, scheduling, task assignment in cloud computing, channel
assignment in wireless networks, 3G communications optimization.
- Data classifiers. A new approach for
finding good data classifiers arises from an infeasibility analysis
algorithm. What is the best way to use this to develop better data
optimization teaching material including lectures, algorithm animations, online
calculators, and sample assignments and solutions.
Web Resources for
Graduate Students: How to organize your thesis, what to expect at a thesis
defense, and other advice.
List of Graduate Students
and Links for Optimization Researchers