Advanced Algorithms, ETH Zurich, Fall 2020

Instructor: Mohsen Ghaffari
Teaching Assistants: Saeed Ilchi, Julian Portmann, and Vaclav Rozhon
Units: 3V+2U+3A, 9 Credits
Lecture Time & Place: Wednesdays 09:00-12:00 (online, see the course moodle for zoom link)
Exercise Session Time & Place: Fridays 10:00-12:00 (online, see the course moodle for zoom link)
Office Hours: By appointment/email
Moodle: Advanced Algorithms 2020 on Moodle
Prerequisite: Sufficient comfort with both (A) Algorithm Design & Analysis and (B) Probability & Basic Inequalities.
For instance, having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, though not required formally.
If you're not sure whether you're ready for this class or not, please try Problem Set 00 and consult the instructor.
Other Links: Information on the Course Catalogue of ETH Zurich


Grading: Two graded homeworks 40%, and a 3-hour final exam 60%.

Homeworks, Problem Sets, and Exercise Sessions: During the semester, we will provide some specially-marked graded homeworks. Each of these graded homeworks will account for 20% of your grade. You should submit your solutions, which must be typeset in LaTeX, via moodle. We will send you more detailed instructions via email. Moreover, we will regularly have other problem sets, roughly one per lecture. You do not need to hand in their solutions. However, we recommed that you try to solve all of these problems on your own, and seek help from the assistants, if needed. These problem sets will be discussed during the exercise sessions. The exercise sessions are an indispensable part of the course -- the material covered in them will be included in the final exam and we strongly recommend attending them.

Lecture Notes:

Here are the lecture notes for this course, but please keep in mind that the lecture notes will be updated throughout the semester. Please see the course moodle for the video recordings of the lectures, and also for the handwritten notes (from the shared screen on the zoom session).

Topic List (14 ⨉ 3-hour lectures, in 5 blocks)

The following shows the schedule of the course in the 2020 edition. The precise schedule and content will be updated throughout the semester. Hollow bullet points indicate additional sources and optional material.

Block 1: Basics of Approximation Algorithms

Block 2: Selected Topics in Approximation Algorithms

Block 3: Streaming & Sketching Algorithms

Block 4: Graph Sparsification

Block 5: Online Algorithms & Competitive Analysis

Additional Material: