General Information Edit

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 Edit


  • MATH 1920 and MATH 2940.


  • ECE 3250 strongly recommended.

Workload Edit

  • 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%)

Advice Edit

  • 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.

Past Offerings Edit

Past Offerings Edit

Semester Time Professor Median Grade

Fall 2018

TR 10:10am-11:25am

Ao Kevin Tang


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