In this introductory optimization for all engineers, you will learn how to utilize optimization to answer critical questions and gain an understanding of the underlying concepts behind today's most effective optimization tools. Optimization models seek to answer what is best under what is possible. You will develop both the theoretical and practical knowledge to create, solve, and interpret optimization models from a broader range of engineering domains. Topics include linear programming, simplex algorithm, nonlinear optimization, interior-point and penalty approaches, derivative-free methods, genetic algorithms, and optimization of systems. A broad range of examples is shown, including problems in transportation, scheduling, machine learning, mechanics, and aerospace. Students successfully completing this course will be able to: - Classify the variety of optimization problems from both a problem formulation and application perspective - Construct an optimization model from a first-principles perspective - Select the appropriate optimization strategy to solve complex engineering optimization problems - Create Matlab (or similar) code to implement some of the basic methods and utilize state-of-the-art solvers.
Prerequisite
Matrices and Linear Equations; MATH 261 (Calculus for Physical Scientists III); These prerequisites may be waived with the consent of the instructor.
Textbooks and Materials
Please check the
CSU Bookstore for textbook information. Textbook listings are available at the
CSU Bookstore about 3 weeks prior to the start of the term.
Instructors
Daniel Herber
daniel.herber@colostate.edu
Dr. Daniel R. Herber is an Associate Professor in the Department of Systems Engineering at Colorado State University in Fort Collins, CO, USA. His research interests and projects have been in design optimization, model-based systems engineering, system architecture, digital engineering, dynamics and control, and combined physical and control system design (control co-design), frequently collaborating with academia, industry, and government laboratories. His work has involved several application domains, including energy, aerospace, defense, and software systems. He teaches courses in model-based systems engineering, system architecture, controls, and optimization. He is a member of INCOSE, ASME, and AIAA. He studied at the University of Illinois at Urbana-Champaign, earning his B.S. (2011) in General Engineering and his M.S. (2014) and Ph.D. (2017) in Systems and Entrepreneurial Engineering. He held a postdoctoral position (2018-2019) with the NSF ERC for Power Optimization for Electro-Thermal Systems (POETS). Learn more at https://www.engr.colostate.edu/~drherber/people#director
