Coursera - Discrete Optimization Review

Discrete optimization is a quasi-self-paced programming course offered by the University of Melbourne through Coursera that is all about solving hard problems. Hard problems in the context of this course means NP-hard problems--problems with exponential worst-case running times. The course differs from most classes on Coursera and elsewhere on the web in that all the materials are available as soon as the course opens, but there is a final deadline for the programming assignments, so it is not a self-paced course in the truest sense. The entire course grade is based on 5 programming assignments: the knapsack problem, graph coloring, traveling salesman, warehouse location and vehicle routing. An average score of 7 (out of 10) on each part of each programming assignment is required to earn a certificate.

Discrete optimization opens with an introductory lecture series on the knapsack problem that lasts a couple of hours followed by three longer lecture series, covering constraint programming, local search and mixed integer programming. The lectures do not need to be viewed in any particular order. Similarly, students can work on the homework projects in any order they choose. This level of freedom is great for students who want to work ahead but it may make it difficult to complete the course if you don't plan ahead because the programming assignments can be very time consuming. The assignment skeleton and submission code is written in Python 2.7, but you can use languages if you want.

The professor, Pascal Van Hentenryck, is extremely energetic and passionate about the subject. He makes the lecture videos surprisingly fun for such a dense subject. The lecture videos themselves are well-made and the professor does a good job explaining the material, although I sometimes felt like the course was trying to cover too many different topics and it wasn't always clear how one would go about applying the methods in lecture to the assignments or using them without using some external package or solver. A little more instruction and direction in that regard would be helpful.

Discrete optimization is challenging course with great programming assignments that introduces many different tools and leaves them on the table for you to play with. The tools don't always with full instruction manuals, so you'll have to figure out many of the details yourself. You won't have time to apply every tool to every problem, but if you focus on one and budget your time well, you'll have a good shot at making it through.

I give discrete optimization 4 out of 5 stars: Very Good.