Course Description
INTRO ENGR OPTIMIZATN TECHNIQS, ENGR 160
4 Units, Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): MATH 010A; CS 010A or EE 020B or ME 018A, ME 018B; for the ENGR 160 online section; enrollment in the Master-in-Science in Engineering program. Introduction to formulating and solving optimization problems in engineering. Includes single-variable and multi-variable optimization; linear programming - simplex method; nonlinear unconstrained optimization-gradient, steepest descent, and Newton methods; and nonlinear constrained optimization - gradient projection methods. Addresses applications of optimization in engineering design problems. Solves various engineering optimization examples using MATLAB. Credit is awarded for one of the following ENGR 160 or EE 284A.
Key Information
Credit: 4 quarter units /
2.67 semester units credit
UC Riverside, College of Engineering
Course Credit:
Upon successful completion, all online courses offered through cross-enrollment provide UC unit credit. Some courses are approved for GE, major preparation and/or, major credit or can be used as a substitute for a course at your campus.If "unit credit" is listed by your campus, consult your department, academic adviser or Student Affairs division to inquire about the petition process for more than unit credit for the course.
UC Berkeley:
Unit Credit
UC Davis:
Course Equivalence: UCD ECI 153
UC Irvine:
Unit Credit
UC Los Angeles:
Unit Credit
UC Merced:
Unit Credit (see your Academic Advisor)
UC Riverside:
Major Requirement: Satisfies Technical Elective for most Electrical Engineering Majors
UC San Diego:
General Education: TMC 1 course toward upper division disciplinary breadth if noncontiguous to major;
Major Requirement: Computer Science and Engineering: Technical elective
UC San Francisco:
Unit Credit
UC Santa Barbara:
Major Requirement: Will apply to Mechanical Engineering upper division major elective requirement
UC Santa Cruz:
Unit Credit
More About The Course
Introduction to formulating and solving optimization problems in engineering. Single- and multi-variable optimization. Decision making and mathematical problem formulation. Formulating and solving linear optimization problems. Formulating and solving integer and mixed-integer optimization problems. Uncertainty and understanding decision making under uncertainty. Formulating and solving nonlinear optimization problems. Applications of optimization in engineering problems. Solving various optimization examples using MATLAB.
Course Creator
Hamed Mohsenian-Rad
Dr. Hamed Mohsenian-Rad is an Associate Professor of Electrical Engineering, an Associate Director of the Winston Chung Global Energy Center, and the Director of the Smart Grid Research Lab at the University of California, Riverside, CA, USA. His research interests include modeling, data analytics, control, and optimization of power systems and smart grids. He has received the National Science Foundation (NSF) CAREER Award, a Best Paper Award from the IEEE Power and Energy Society (PES) General Meeting, and a Best Paper Award from the IEEE International Conference on Smart Grid Communications. Two of his journal papers are among the top five most cited articles in the field of Smart Grid, each with over 1000 citations. Dr. Mohsenian-Rad received his Ph.D. degree in Electrical and Computer Engineering from the University of British Columbia, Vancouver, Canada, in 2008. He currently serves as an Editor for the IEEE Transactions on Smart Grid and IEEE Power Engineering Letters.
Dr. Hamed Mohsenian-Rad is an Associate Professor of Electrical Engineering, an Associate Director of the Winston Chung Global Energy Center, and the Director of the Smart Grid Research Lab at the University of California, Riverside, CA, USA. His research interests include modeling, data analytics, control, and optimization of power systems and smart grids. He has received the National ...Dr. Hamed Mohsenian-Rad is an Associate Professor of Electrical Engineering, an Associate Director of the Winston Chung Global Energy Center, and the Director of the Smart Grid Research Lab at the University of California, Riverside, CA, USA. His research interests include modeling, data analytics, control, and optimization of power systems and smart grids. He has received the National Science Foundation (NSF) CAREER Award, a Best Paper Award from the IEEE Power and Energy Society (PES) General Meeting, and a Best Paper Award from the IEEE International Conference on Smart Grid Communications. Two of his journal papers are among the top five most cited articles in the field of Smart Grid, each with over 1000 citations. Dr. Mohsenian-Rad received his Ph.D. degree in Electrical and Computer Engineering from the University of British Columbia, Vancouver, Canada, in 2008. He currently serves as an Editor for the IEEE Transactions on Smart Grid and IEEE Power Engineering Letters.
