Teachers
Teaching Assistants
When and where Monday 8.1512, Bldg. 358, Room 060a
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).
Gradescope We use Gradescope for correcting and scoring exercises. The system significantly improves consistency and quality in correcting. Please sign up for Gradescope as follows:
ZZ5ZDB
, your full name, your @student.dtu.dk
email, and your studentid (of the form s123456
). Please follow these instructions precisely so that we can correctly identify you.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  Teacher  Slides  Weekplan  Material 

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


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


Geometry: Range Reporting, Range Trees, and kD Trees  Philip  1x1 · 4x1  Range Reporting 


Amortized Data Structures I: Splay trees  Eva  1x1  Amortized I  Notes by Jeff
Erickson: scapegoats
and splaytrees Paper by Sleator and Tarjan: Selfadjusting binary search trees. See also: Brinkmann, Degraer, de Loof on practical performance of splay trees. More exercises on amortized algorithms in 02110. 

Integer Data Structures III: Lowest Common Ancestor, Range Minimum Query  Inge  1x1 · 4x1  LCA and RMQ 


Trees: Level Ancestor, Path Decompositions, Tree Decompositions  Inge  1x1 · 4x1  Level Ancestor 


Amortized Data Structures II: Dynamic Graph Orientation  Eva  1x1  Amortized II  Dynamic
Graph Orientations by Brodal and Fagerberg See also: Berglin & Brodal, Christiansen & Rotenberg 

Strings I: Dictionaries, Tries, Suffix trees  Inge  1x1 · 4x1  Suffix Trees 


Strings II: Radix Sorting, Suffix Array, Suffix Sorting  Philip  1x1 · 4x1  Suffix Sorting 


Compression: LempelZiv, RePair, Grammars, Compressed Computation  Philip  1x1 · 4x1  Compression 


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


Approximation Algorithms II: TSP, Set cover  Inge  1x1 · 4x1  Approximation Algorithms II 


Course Roundup, Questions, Future Perspectives 
The course features number of nonmandatory/voluntary handin exercises that are posted throughout the course. These are handed in as follows.
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 3 pages. To submit your solution:
The course features a few mandatory exercises during the course. The exercises are handedin according to the same guidelines as the nonmandatory/voluntary handin exercises, except that the page limit may be different.
Collaboration policy for the mandatory exercise
How should I write my exercises? The ideal writing format for 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 exercise 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 nontrivial 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.