263-4400-00L  Advanced Graph Algorithms and Optimization

SemesterSpring Semester 2020
LecturersR. Kyng
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 30.


263-4400-00 GAdvanced Graph Algorithms and Optimization3 hrs
Wed09:15-12:00CAB G 52 »
R. Kyng
263-4400-00 AAdvanced Graph Algorithms and Optimization1 hrsR. Kyng

Catalogue data

AbstractThis course will cover a number of advanced topics in optimization and graph algorithms.
ObjectiveThe 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.
ContentStudents 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 / NoticeThis 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

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits5 credits
ExaminersR. Kyng
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationGraded 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 linkInformation
Only public learning materials are listed.


No information on groups available.


Places30 at the most
Waiting listuntil 01.03.2020

Offered in

CAS in Computer ScienceFocus Courses and ElectivesWInformation
Cyber Security MasterElective CoursesWInformation
Data Science MasterCore ElectivesWInformation
Doctoral Department of MathematicsGraduate SchoolWInformation
Computer Science MasterFocus Elective Courses Theoretical Computer ScienceWInformation
Computer Science MasterElective Focus Courses General StudiesWInformation
Mathematics MasterSelection: Theoretical Computer Science, Discrete MathematicsWInformation