Introduction to stochastic control, with applications taken from a variety of areas including supply-chain optimization, advertising, finance, dynamic resource allocation, caching, and traditional automatic control. Markov decision processes, optimal policy with full state information for finite-horizon case, infinite-horizon discounted, and average stage cost problems. Bellman value function, value iteration, and policy iteration. Approximate dynamic programming. Linear quadratic stochastic control.
The materials for this course were written by Professors Stephen Boyd, Sanjay Lall, and Benjamin Van Roy at Stanford.
This year the course is taught by Professor Sanjay Lall
Lectures Tuesdays and Thursdays, 9:00 - 10:20am in 200-034
Review Sessions Fridays, 3:00 - 4:00pm in Hewlett 102
EE266 is the same as MS&E251, Stochastic Decision Models.
EE266 was numbered EE365 in previous years. We'll use most of last year's notes, but add some new sections too.
The final exam will be a 24hr take-home. It will be available for pickup at:
Friday Jun 3, 5pm
Monday Jun 6, 10am or 5pm
Tuesday Jun 7, 10am or 5pm
Wednesday Jun 8, 10am