Science 599.60 / 601.60 - Randomized Algorithms
2007: I won't post news on this website
anymore. All information and
news for this course is now availble in the course forum.
In this course we study
algorithms that make random choices as they proceed. Over the last
decades, such randomized (or probabilistic) algorithms have found
widespread application in essentially all areas of computing, and
randomization has become one of the most powerful tools in algorithm
design. For many applications, randomized algorithms are available,
which are simpler or faster (or both) than any known deterministic
This course will explore
techniques for designing and analyzing randomized algorithms, and will
provide examples from a variety of problem areas. Topics will include
Basic knowledge in
probability theory is required (e.g., the notion of random variables,
expectation, independence, etc.). More advanced material will be
introduced whenever necessary.
- Fundamental techniques for the design and analysis of
randomized algorithms (lower bound techniques, moments and deviations,
tail inequalities, the probabilistic method)
- Applications (data structures, load balancing, network
algorithms, and others)
- Randomized complexity classes
Motwani and P. Raghavan, Cambridge University Press 1995. ISBN
- Additional reading:
Computing - Randomized Algorithms and Probabilistic Analysis, M. Mitzenmacher and E. Upfal,
Cambridge University Press 2005. ISBN 0-521-83540-2.
- Consent of the department.
- Basic knowledge in
probability theory (Math 321 is recommended).
- Practice in the
analysis of algorithms
(CPSC 413 is strongly recommended).
- You might not get
credit for this course if you have received credit for CPSC 517 from
Winter 2007 (other years are o.k.).
Time: Tuesdays and
Location: ENC 123
See my homepage.
Tuesdays and Thursdays,
14:00-15:00pm (ICT 655), or by appointment.
Please approach me right after class if you want to meet me during my
regular office hours.
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