The purpose of this course is to develop mathematical, modeling, and computational skills for optimization. The emphasis in this video is modeling.
This video introduces two common and powerful techniques for solving problems that involve mixed integer linear programming (MILP). The first is conversion of a nonlinear constrained optimization problem into a MILP by piecewise linear approximation. As a result of the conversion, the original problem can be approximately solved to global optimality. The second is the introduction of binary variables to solve chance constrained optimization problems. As a result, problems involving probabilistic constraints can be solved to global optimality.