MGT-418: Convex Optimization for MSc and PhD students
Convex optimization is a fundamental branch of applied mathematics that has applications in almost all areas of engineering, the basic sciences and economics. For example, it is not possible to fully understand support vector machines in statistical learning, nodal pricing in electricity markets, the fundamental welfare theorems in economics, or Nash equilibria in two-player zero-sum games without understanding the duality theory of convex optimization. The course primarily focuses on techniques for formulating decision problems as convex optimization models that can be solved with existing software tools. The exact formulation of an optimization model often determines whether the model can be solved within seconds or only within days, and whether it can be solved for ten variables or up to 10^6 variables.

MGT-483: Optimal Decision Making for MSc students
This course introduces the quantitative methods of management science. We develop tools and concepts of optimization and decision analysis that enable managers in manufacturing, service operations, marketing, transportation and finance to transform data into insights for making better decisions.