A generalized optimization model; discrete and continuous state spaces; deterministic and stochastic transition functions. Multistage decision processes. Functional equations and successive approximation in function and policy spaces. Relationship to linear programming and acyclic networks. Markovian decision processes. Solution methods and computational problems. Associated topics and applications such as calculus of variations; feedback control processes; and optimal trajectories, inventory and maintenance policies, and stopping rules. Prereq: CS 525 or ISyE 623; Math 521 or CS 726; Math 431 and computer programming, or consent of instructor.