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:
WYBKJ3, your full name, your
@student.dtu.dkemail, and your student-id (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.
|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||
|Course Roundup, Questions, Future Perspectives|
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
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.