This course builds on the foundations of Optimization I and explores advanced topics in optimization and decision-making. Students study extensions of advanced linear and integer programming, large-scale and decomposition methods, and modern algorithmic approaches. The course may include topics such as optimization under uncertainty, stochastic programming, dynamic programming, and sequential decision problems. Applications to real-world systems and the use of advanced optimization software are emphasized throughout.
