Stochastic Programming Approach to Optimization Under Uncertainty - Part 2

Explore advanced concepts in optimization under uncertainty in this lecture from the Theory of Reinforcement Learning Boot Camp. Delve into topics such as dynamical programming, stagewise independent discretization, approximation techniques, cutting planes, trial points, and policy rules. Understand the underlying principles and effectiveness of these methods, and examine their applications in multistage problems. Investigate duality theory and its relevance to stochastic programming. Engage with thought-provoking questions and discussions to deepen your understanding of this complex field.

Dynamical Programming
Stagewise Independent
Discretization
Approximation
Cutting Planes
Trial Points
Policy Rule
Why does it work
Duality
Questions
Multistage problems
Duals
Question