EE365: Stochastic Control

Spring Quarter 2014

Course description

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.

Prerequisites: Linear algebra (as in EE263) and probability (as in EE178 or MS&E220).


Professor Sanjay Lall and teaching assistants Samuel Bakouch, Alex Lemon and Paris Syminelakis.