Electrical Engineering 127A — Optimization Models and Applications (0 Units)

Course Overview


The course introduces various optimization models in engineering. Topics discussed include linear, quadratic convex, and second-order cone optimization. Various applications are also discussed, which include circuit design, signal processing, finance operations research, machine learning, computer science, and bioengineering.


Math 54; additional linear algebra knowledge equivalent to Math 110 helps.

Topics Covered

  • Vectors
  • matrices
  • least-square problems
  • principal component analysis
  • singular value decomposition
  • convexity
  • linear programming
  • quadratic programming
  • second-order cone programming
  • robust LP/QP
  • geometric programming
  • duality
  • applications


Course Work

The course includes one midterm, one quiz, and a final. There are around 4-6 problem sets, assigned in two week intervals.

Time Commitment

3 hours of lecture, 1 hour of discussion per week. The problem sets should take around 4-6 hours to complete, or even more depending on the particular assignment.

HKN Tips

Having some linear algebra knowledge coming into the course helps, as the first part of the course covers the linear algebra fundamentals needed for topics like SVD and EVD. If you are rusty with Math 54 materials, make sure to review them early on in the course, as that will help you follow better.

Choosing the Course


Convex optimization

When to take

This class is usually taken during the junior or senior year. Taking it early will allow you to move on to advanced courses such as EE227 series.

What next?

EE227 series, which dive into deeper optimization algorithms and approximations.

Usefulness for Research or Internships

Concepts learned in this course is highly relevant in machine learning (one of the applications). Therefore, having this course under your belt will give you a competitive edge for research in relevant fields. In terms of internships, since optimization is often used in search algorithms and machine learning, this course will become a plus when you interview for companies that deal with search engines and user ads, etc.. Nonetheless, fields outside of technology, such as finance and marketing, also use optimization concepts learned in this class.

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