General Information
Introduction to optimization theory and algorithms for system analysis and design. Topics include linear programming, convex programming, duality. We may touch dynamic programming around the end if time permits. Application will be discussed in various areas including geometric problems, networks, control, circuits, signal processing, and communications. This course is ideal for students who have not had an optimization course but want to have an idea of the subject within one semester.
Prerequisites
Prerequisites:
- MATH 1920 and MATH 2940.
Corequisites:
- ECE 3250 strongly recommended.
Workload
- 5 problem sets
- Quick in-class quizzes (about one every 2 weeks)
- Take-home midterm and final
- Fall 2018:
- 6 problem sets (60%)
- Problem sets contained a combination of analytical questions and numerical programming questions
- 2 in class prelims (40%)
- 6 problem sets (60%)
Advice
- The class requires a pretty strong knowledge of calculus and linear algebra. None of the concepts are too difficult, but the problem sets and exams require a significant amount of time to write and debug MATLAB code.