ENGR 597 is a special-topics course that is offered on an as-needed basis with changing course topics. For the Fall 2019 semester, this course will provide a survey of analytical and numerical methods for solving multi-stage decision problems which include uncertainty, with the goal of operating systems to minimize an undesirable outcome (e.g., economic cost, risk) over a number of stages. The course will introduce the dynamic programming framework and illustrate its use in solving multi-stage operational decision problems in areas such as energy, finance, and operations research. Finite horizon and infinite horizon problems will be discussed. This course will include individual review assignments to survey existing literature and application of concepts through numerical simulation.
If you are interested in taking this special topics course, please contact the instructor to enroll.
Course can apply to: SE ME, MS, PhD, and DENG.
This course can be applied toward:
Students must have taken a previous course in undergraduate probability theory and preferably graduate coursework in probability and/or stochastic processes. Undergraduate engineering mathematics (calculus, differential equations, matrices). Proficiency in one or more of these languages: MATLAB, Python, R. Recommended preparation: previous coursework in undergraduate or graduate control theory.
Military personnel admitted to a College of Engineering online degree program may be eligible for a 15% tuition discount. Tuition discounts can only be given if you provide the appropriate discount code at the time of registration. Call (877) 491-4336 or email email@example.com to learn more.
Textbooks and Materials
Textbooks and materials can be purchased at the CSU Bookstore unless otherwise indicated.
- Dynamic Optimization: The Calculus of Variations and Optimal Control in Economics and Management (1991)
M. Kamien and N. Schwartz
- Dynamic Programing and Optimal Control (Vol. 1) (2017)
Dr. James Cale is an Associate Professor in the Mechanical Engineering Department / Systems Engineering Program at Colorado State University. His research focuses on modeling, control and design optimization of energy sources and systems. His background and interests are in the areas of energy conversion, power-electronic drive systems, microgrids, finite-inertia power systems, computational and applied electromagnetics, biologically-inspired optimization methods, hardware-in-the-loop, and machine learning algorithms.
Prior to joining CSU, he led the Integrated Devices & Systems group at the National Renewable Energy Laboratory in Golden, Colorado. Before that he worked in senior design engineering roles at Advanced Energy Industries and Orbital ATK (since acquired by Northrop Grumman). James earned his doctorate in electrical engineering (with honors) from Purdue University, where he was funded by an NSF IGERT fellowship. He earned his BSEE from Missouri University of Science & Technology (summa cum laude). He is a member of Tau Beta Pi, Mensa International, and is a Senior Member of IEEE.