This course covers theoretical foundations that are necessary for advanced study of networking. It focuses on algorithm design and optimization techniques that are most commonly used to solve complex networking problems. Major topics include complexity analysis with applications to networking problems, design and proof of approximation algorithms, design of meta-heuristic algorithms, formulation techniques for network optimization, linear and non-linear optimization techniques with applications to networking, design of distributed algorithms with proof of convergence for networks systems.
Modern communication networking is a multi-disciplinary subject in that it draws upon theories and algorithms from multiple disciplines. Traditionally, there is a lack of systematic treatment of theoretical foundation for graduate education in networking area. The purpose of this course is to provide graduate students the most essential theoretical training in algorithm design and optimization techniques that are most commonly used to solve complex problems in networking. To achieve this objective, the course covers topics on algorithms and optimizations that are most relevant to address theoretical problems in networking. Case studies are provided for each of these techniques. With this set of analytical tools, the graduate students are expected to be prepared to address complex problems in network systems.
Percentage of Course
|Complexity theory with applications to networking problems||20%|
|Design and proof of approximation algorithms||20%|
|Design of meta-heuristic algorithms||20%|
|Formulation techniques for network optimization||10%|
|Linear and non-linear optimization techniques with applications to networking||20%|
|Design of distributed algorithms with proof of convergence for network systems||10%|