Basic Optimization

AY2018/2019 Semester 2

This is a first course in mathematical optimization. It builds the basic knowledge and skills in the theory and techniques of analysing and solving simple optimization models. With these foundations, you will be able to deepen your understanding of more complex optimization models, and their applications to various disciplines in subsequent mathematical optimization and operations research courses. Course Content: a. The simplex method, the revised simplex method, and the two-phase simplex method. b. Minimum-cost flow problem and the network simplex method. c. Linear programming duality, Complementary Slackness, and theorems of alternatives. d. Sensitivity and post-optimality analysis of linear programs. e. Lagrange duality, convex programming, and necessary and sufficient Karush-Kuhn-Tucker conditions.