263-4400-00L Advanced Graph Algorithms and Optimization
| Semester | Spring Semester 2020 |
| Lecturers | R. Kyng |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Number of participants limited to 30. |
Courses
| Number | Title | Hours | Lecturers | ||||
|---|---|---|---|---|---|---|---|
| 263-4400-00 G | Advanced Graph Algorithms and Optimization | 3 hrs |
| R. Kyng | |||
| 263-4400-00 A | Advanced Graph Algorithms and Optimization | 1 hrs | R. Kyng |
Catalogue data
| Abstract | This course will cover a number of advanced topics in optimization and graph algorithms. |
| Objective | The course will take students on a deep dive into modern approaches to graph algorithms using convex optimization techniques. By studying convex optimization through the lens of graph algorithms, students should develop a deeper understanding of fundamental phenomena in optimization. The course will cover some traditional discrete approaches to various graph problems, especially flow problems, and then contrast these approaches with modern, asymptotically faster methods based on combining convex optimization with spectral and combinatorial graph theory. |
| Content | Students should leave the course understanding key concepts in optimization such as first and second-order optimization, convex duality, multiplicative weights and dual-based methods, acceleration, preconditioning, and non-Euclidean optimization. Students will also be familiarized with central techniques in the development of graph algorithms in the past 15 years, including graph decomposition techniques, sparsification, oblivious routing, and spectral and combinatorial preconditioning. |
| Prerequisites / Notice | This course is targeted toward masters and doctoral students with an interest in theoretical computer science. Students should be comfortable with design and analysis of algorithms, probability, and linear algebra. Having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, but not formally required. If you are not sure whether you're ready for this class or not, please consult the instructor. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 5 credits |
| Examiners | R. Kyng |
| Type | graded semester performance |
| Language of examination | English |
| Repetition | Repetition only possible after re-enrolling for the course unit. |
| Additional information on mode of examination | Graded Homework 1: 15 % of grade. Homework consists entirely of exercises. Graded Homework 2: 15 % of grade. 10 % is for exercises, 5 % is for a short written report on a paper. The lecturer will assign papers to students, possibly with some input from students. Assignments are done individually. Oral exam: 70 % of the grade. Exam is 30 minutes, of which 15 minutes will be spent on the paper associated with the written report, and 15 minutes will be spent covering topics from the lectures. |
Learning materials
| Main link | Information |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| Places | 30 at the most |
| Waiting list | until 01.03.2020 |
