MS&E351
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Dynamic Programming and Stochastic Control
MGMTSCI
ENGR - School of Engineering
Course Description
Markov population decision chains in discrete and continuous time. Risk posture. Present value and Cesaro overtaking optimality. Optimal stopping. Successive approximation, policy improvement, and linear programming methods. Team decisions and stochastic programs; quadratic costs and certainty equivalents. Maximum principle. Controlled diffusions. Examples from inventory, overbooking, options, investment, queues, reliability, quality, capacity, transportation. MATLAB. Prerequisites: MATH 113, 115; Markov chains; linear programming.
Grading Basis
ROP - Letter or Credit/No Credit
Min
3
Max
3
Course Repeatable for Degree Credit?
No
Course Component
Lecture
Enrollment Optional?
No
Does this course satisfy the University Language Requirement?
No
Programs
MS&E351
is a
completion requirement
for: