Lecture, three hours; discussion, one hour. Enforced requisites: courses 115A, 131A. Not open for credit to students with credit for former Electrical Engineering 136. Fundamentals of optimization. Linear programming: basic solutions, simplex method, duality theory. Unconstrained optimization, Newton method for minimization. Nonlinear programming, optimality conditions for constrained problems. Additional topics from linear and nonlinear programming. P/NP or letter grading.

Review Summary

Clarity
10.0 / 10
Organization
10.0 / 10
Time
5-10 hrs/week
Overall
10.0 / 10

Reviews

    Quarter Taken: Spring 2023 In-Person
    Grade: A+

    Professor Meng is very experienced in the field of convex optimization; her lectures can be a bit technical but they do present concepts and algorithms in an organized way. Her exams are very challenging and she does not curve. However, if you put some work into the class and really understand how things work, they should be fine. By the way, Professor Meng is very friendly and always willing to help during office hours. Overall I would recommend the class for the academic depth and challenge it offers.

    Quarter Taken: Fall 2023 In-Person
    Grade: A

    Professor Meng is the goat, talks about a lot of content in class, with a heavy focus on algorithm, but her lecture is clear and inspiring. The content you learnt in this class will be of great help if you are going into machine learning.

    Quarter Taken: Fall 2023 In-Person
    Grade: A

    Very helpful professor even if it was a somewhat challenging class! I was surprised at how well I was able to do.

Course

Instructor
Tingwei Meng
Previously taught
24S 24W 23F 23S

Grading Information

  • No group projects

  • Attendance not required

  • 1 midterm

  • Finals week final

  • 100% recommend the textbook

Previous Grades

Grade distributions not available.