John W. Chinneck

Distinguished Research Professor. Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada.

RESEARCH INTERESTS

Optimization algorithms and software. Faster and more effective algorithms and software for nonlinear, mixed-integer, global, 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, task assignment in cloud computing, channel assignment in wireless networks, 3G communications optimization.

Data classifiers. A new approach for finding good data classifiers and for selecting features arises from an infeasibility analysis algorithm. What is the best way to use this to develop better data classifiers?

Teaching optimization. Visit optimization101.org for teaching and learning resources for introductory optimization, including chapters, algorithm animations, online calculators, example assignments with solutions, and pointers to online resources.

Contact: chinneck@sce.carleton.ca