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School of Computing · ANU College of Systems and Society
This course covers a large spectrum of algorithmic topics such as approximation algorithms, randomized algorithms, parallel/distributed algorithms, and online algorithms. Students will learn how to analyze the most fundamental algorithms in these areas using various techniques from graph theory, probability theory, and algebra. After successful completion of the course, students will be able to design efficient and effective algorithms for similar problems leveraging these techniques. Furthermore, students will learn about the most recent research advancements in these areas by studying current research publications.
Mid-semester test
Learning outcomes 1, 2, 4
Assignment
Learning outcomes 5
Final Exam
Learning outcomes 1, 2, 3, 4
Students are expected to recap and conduct independent studies to gain an understanding of the materials, this study combined with the lectures and tutorials will total 130 hours.
See Class Summary Preliminary Reading - Vijay V Vazirani. Approximation algorithms, volume 1. Springer, 2001. - David P Williamson and David B Shmoys. The design of approximation algorithms. Cambridge university press, 2011. - Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein. Introduction to algorithms. MIT press, 2022. - Rajeev Motwani and Prabhakar Raghavan. Randomized algorithms. Cambridge university press, 1995. - Allan Borodin and Ran El-Yaniv. Online computation and competitive analysis. Cambridge university press, 2005. - David Peleg. Distributed computing: a locality-sensitive approach. SIAM, 2000.