General Info

Teachers

• Inge Li Gørtz, inge@dtu.dk
• Eva Rotenberg, erot@dtu.dk
• Philip Bille, phbi@dtu.dk, office hours Fridays 12.00-12.30 on Zoom id: 677 3898 7472

Teaching Assistants

• Aleksander Bjørn Grodt Christiansen
• Tord Joakim Stordalen

When and where Mondays 8.15-12.00 in Bldg. 421, Aud. 73.

Prerequisites Undergraduate level courses in algorithms and data structures (comparable to 02105 + 02110) and mathematical maturity. You should have a working knowledge of algorithm analysis (e.g. asymptotic notation, worst case analysis, amortized analysis, basic analysis of randomized algorithms), data structures (e.g. trees, heaps, priority queues, hash tables, balanced binary search trees), graph algorithms (e.g. BFS, DFS, single source shortest paths, minimum spanning trees, topological sorting), dynamic programming, divide-and-conquer, and NP-completeness (e.g. basic reductions).

Weekplan

The weekplan is preliminary. It will be updated during the course. Under each week there is a number of suggestions for reading material regarding that weeks lecture. It is not the intention that you read all of the papers. It is a list of papers and notes where you can read about the subject discussed at the lecture.

Mandatory Exercises

Use the template.tex file to prepare your write up your solution to the exercises. Do not repeat the problem statement in your solutions and do not modify the template. Compile your solutions using LaTeX. The maximum size of the finished pdf must be at most 2 pages. To submit your solution:

Collaboration policy for mandatory exercises

• The assignment is individual.
• Up to three students can collaborate. Collaborators must be listed in your solution (see template).
• Collaboration between students are limited to discussion of ideas.
• Do not use or seek out solutions from previous years of the course, solutions from similar courses, or solutions found on the internet.

Violation of the collaboration policy is strictly prohibited.

Frequently Asked Questions

How can I access the listed reading material? Why are some of the links are behind a paywall? We typically link to material using standard doi's or publication venue links. We do this since these are stable over time and allow you to uniquely identify the material. To access these (at no cost) you sometimes need to use academic search engines or library services provided by DTU. Ask your teacher if you are unfamiliar with how to use these tools.

What do I do if I want to do a MSc/BSc thesis or project in Algorithms? Great! Algorithms is an excellent topic to work on :-) and Algorithms for Massive Data Sets is designed to prepare you to write a strong thesis. Some basic tips and points.

• Let us know well in advance: Identifying an interesting problem in algorithms that matches your interest can take time. With enough time to go over the related litterature and study up on relevant topics your project will likely be more succesful. It may also be a good idea to do an initial “warm up” project before a large thesis to test ideas or survey an area.
• Join the community: It is very good idea to enter the local algorithms community at DTU and the Copenhagen area to get a feel for what kind of stuff you could work on for your thesis and what thesis work algorithms is about. Talk to other students doing thesis work in algorithms. Go to algorithms talks and thesis defenses in algorithms.
• Collaborate: We strongly encourage you to do your thesis in pairs. We think that having a collaborator to discuss with greatly helps in many aspects of thesis work in algorithms. Our experience confirms this.
• No strings attached: Choosing a topic for your thesis is important. You are welcome to discuss master thesis topics with us without pressure to actually write your thesis in algorithms. We encourage you to carefully select your topic.