General Info


Teaching Assistants

When and where Monday 8.15-12, Bldg. XXX, Aud. XXX

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. stacks, queues, linked lists, trees, heaps, priority queues, hash tables, balanced binary search trees, tries), 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).

Gradescope We use Gradescope for correcting and scoring the mandatory exercises. The system significantly improves consistency and quality in correcting. Please sign up for Gradescope as follows:

  1. Go to
  2. Select "Sign up for free".
  3. Select "Sign up as a student".
  4. Enter the course entry code WYBKJ3, your full name, your email, and your student-id (of the form s123456). Please follow these instructions precisely so that we can correctly identify you.
If you do not wish to sign up for Gradescope please contact a course responsible.


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.

Week Topics Slides Weekplan Material
Introduction and Warm-up Warm Up
Integer Data Structures I: Dictionaries, Universal and Perfect Hashing. 1x1 · 4x1 Hashing
Integer Data Structures II: Predecessor Problem, van Emde Boas, x-Fast and y-Fast Tries 1x1 · 4x1 Predecessor
Integer Data Structures III: Nearest Common Ancestor, Range Minimum Query 1x1 · 4x1 LCA and RMQ
Geometry: Range Reporting, Range Trees, and kD Trees 1x1 · 4x1 Range Reporting
Trees: Level Ancestor, Path Decompositions, Tree Decompositions 1x1 · 4x1 Level Ancestor
Strings I: Dictionaries, Tries, Suffix trees 1x1 · 4x1 Suffix Trees
Strings II: Radix Sorting, Suffix Array, Suffix Sorting 1x1 · 4x1 Suffix Sorting
Compression: Lempel-Ziv, Re-Pair, Grammars, Compressed Computation 1x1 · 4x1 Compression
Approximation Algorithms I: Introduction to approximation algorithms, scheduling and k-center. 1x1 · 4x1 Approximation Algorithms I
Approximation Algorithms II: TSP, Set cover 1x1 · 4x1 Approximation Algorithms II
Approximation Algorithms III: Vertex cover, inapproximability results 1x1 Approximation Algorithms III
  • Algorithm Design, Kleinberg and Tardos, Addison-Wesley, section 11.4 (on DTU Learn).
  • The Design of Approximation Algorithms, Williamson and Shmoys, Cambridge Press, section 2.2 (proof of Thm 2.4) and 2.3 (proof of Thm. 2.9).
Course Roundup, Questions, Future Perspectives

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

Violation of the collaboration policy is strictly prohibited.

Frequently Asked Questions

How should I write my mandatory exercises? The ideal writing format for mandatory exercises is classical scientific writing, such as the writing found in the peer-reviewed articles listed as reading material for this course (not textbooks and other pedagogical material). One of the objectives of this course is to practice and learn this kind of writing. A few tips:

How much do the mandatory exercises count in the final grade? The final grade is an overall evaluation of your mandatory exercise and the oral exam combined. Thus, there is no precise division of these part in the final grade. However, expect that (in most cases, and under normal circumstances) the mandatory exercises account for a large fraction of the final grade.

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.