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.
• 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.
• 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?
• See links below for more information.

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