Advanced Topics in Linear and Integer Programming

3 credits · Graduate

The course will explore advanced methods to deal with large-scale and/or uncertain linear and integer programming problems encountered in real-life applications. Large-scale optimization methods include: Dantzig-Wolfe decomposition, column generation, Lagrangian relaxation, cutting planes, branch-and- cut, branch-and-price and Benders’ decomposition. Methods for dealing with uncertainty include: stochastic programming, chance-constrained programming, sample-average approximation, robust optimization and distributionally-robust optimization. Through a project, students will get a chance to apply these methods on problems drawn from different applications such as logistics, energy and finance PREREQUISITES: IENG 6912 or instructor permission. FORMATS: Lecture