Research interests
- Optimization algorithms and
software.
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
assistants.
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
classifiers?
Links
Biography
Publications
Optimization101.org: Introductory
optimization teaching material including lectures, algorithm animations, online
calculators, and sample assignments and solutions.
Web Resources for
Optimization Students
For
Graduate Students: How to organize your thesis, what to expect at a thesis
defense, and other advice.
List of Graduate Students
Software
and Links for Optimization Researchers