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# 252-0417-00L  Randomized Algorithms and Probabilistic Methods

 Semester Autumn Semester 2016 Lecturers A. Steger, E. Welzl Periodicity yearly recurring course Language of instruction English

### Catalogue data

 Abstract Las Vegas & Monte Carlo algorithms; inequalities of Markov, Chebyshev, Chernoff; negative correlation; Markov chains: convergence, rapidly mixing; generating functions; Examples include: min cut, median, balls and bins, routing in hypercubes, 3SAT, card shuffling, random walks Objective After this course students will know fundamental techniques from probabilistic combinatorics for designing randomized algorithms and will be able to apply them to solve typical problems in these areas. Content Randomized Algorithms are algorithms that "flip coins" to take certain decisions. This concept extends the classical model of deterministic algorithms and has become very popular and useful within the last twenty years. In many cases, randomized algorithms are faster, simpler or just more elegant than deterministic ones. In the course, we will discuss basic principles and techniques and derive from them a number of randomized methods for problems in different areas. Lecture notes Yes. Literature - Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995)- Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005)

### Performance assessment

 Performance assessment information (valid until the course unit is held again) Performance assessment as a semester course ECTS credits 7 credits Examiners A. Steger, E. Welzl Type end-of-semester examination Language of examination English Repetition The performance assessment is only offered at the end after the course unit. Repetition only possible after re-enrolling. Additional information on mode of examination Exercises 30% to the final grade: In week 4, 7 and 10 of the term (roughly) we will hand out a specially marked exercise, whose solution (typeset in LaTeX or similar) is due two weeks later. These solutions will be graded; the grades will each account for 10% of the final grade. End of term: written exam (180 min) accounting for 70% of the grade; open book exam: you are allowed to consult any books, handouts, and personal notes. The use of electronic devices is not allowed.

### Learning materials

 No public learning materials available. Only public learning materials are listed.

### Courses

NumberTitleHoursLecturers
252-0417-00 VRandomized Algorithms and Probabilistic Methods3 hrs
 Tue 13-14 CAB G 51 » Thu 08-10 CAB G 51 »
A. Steger, E. Welzl
252-0417-00 URandomized Algorithms and Probabilistic Methods2 hrs
 Tue 16-18 CAB G 51 »
A. Steger, E. Welzl
252-0417-00 ARandomized Algorithms and Probabilistic Methods
Project Work, no fixed presence required.
1 hrsA. Steger, E. Welzl

### Groups

 No information on groups available.

### Restrictions

 There are no additional restrictions for the registration.

### Offered in

ProgrammeSectionType
Certificate of Advanced Studies in Computer ScienceFocus Courses and ElectivesW
Doctoral Dep. of Information Technology and Electrical EngineeringDoctoral and Post-Doctoral CoursesW
Computer Science TCSpecialized Courses in Respective Subject with Educational FocusW
Computer Science Teaching DiplomaSpec. Courses in Resp. Subj. w/ Educ. Focus & Further Subj. DidacticsW
Computer Science MasterFocus Core Courses Theoretical Computer ScienceW
Mathematics BachelorAuswahl: Theoretical Computer ScienceW
Mathematics MasterAuswahl: Theoretical Computer ScienceW
Computational Science and Engineering BachelorElectivesW
Computational Science and Engineering TCFurther Subject DidacticsW
Computational Science and Engineering MasterElectivesW