Teachers
When and where Monday 8.1512, Bldg. 421, Aud. 72.
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, divideandconquer, and NPcompleteness (e.g. basic reductions).
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  Mandatory  Material 

Integer Data Structures I: Dictionaries, Universal and Perfect Hashing.  1x1 · 4x1  Hashing 


Integer Data Structures II: Predecessor Problem, van Emde Boas, xFast and yFast Tries  1x1 · 4x1  Predecessor  X 


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  X 


Trees: Level Ancestor, Path Decompositions, Tree Decompositions  1x1 · 4x1  Level Ancestor 
 
Strings I: Dictionaries, Tries, Suffix trees  1x1 · 4x1  Suffix Trees  X 
 
Strings II: Radix Sorting, Suffix Array, Suffix Sorting  1x1 · 4x1  Suffix Sorting 
 
Compression: LempelZiv, RePair, Grammars, Compressed Computation  1x1 · 4x1  Compression  X 


Approximation Algorithms I: Introduction to approximation algorithms, scheduling and TSP.  1x1 · 4x1  Approximation Algorithms I 


Approximation Algorithms II: kcenter  1x1 · 4x1  Approximation Algorithms II  X 


Dynamic graph algorithms I: Introduction to dynamic graphs, EvenShiloach trees  Dynamic Graphs I 


Graph algorithms II: Efficient algorithm for vertex cut.  pptx 1x1 · 4x1 1x1 
Graph Algorithms II  X  
Course Roundup, Questions, Future Perspectives 
Use the template.tex file to prepare your hand in exercises. Do not repeat the problem statement in your hand in. Compile using LaTeX. Upload the resulting pdf file (and only this file) via DTU Learn. The maximum size of the finished pdf must be at most 2 pages. An exercise from week x must be handed in no later than Sunday in week x before 20.00.
Collaboration policy for mandatory exercises
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 peerreviewed 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.
Can I write my assignments in Danish? Ja. Du er meget velkommen til at aflevere på dansk.
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.