NDAK14005U  Randomized Algorithms

Volume 2014/2015
Content

Randomized algorithms are often far superior to their traditional deterministic counterparts, both in efficiency and simplicity. Many computational tasks are fundamentally impossible without randomization. However, mastering randomized algorithms requires a basic understanding of the relevant combinatorial probability theory, and therefore a regular algorithms course will normally either skip them, or teach them very superficially. Randomization is a way of thinking, that needs a proper introduction. Applications in many areas will be considered, e.g., graph algorithms, machine learning, distributed computing, and geometry, but the focus will be on the general understanding, the goal being to give the students the foundation needed to understand and use randomization, no matter what application area they may later be interested in.

  • Game Theoretic Techniques
  • Moments and Deviations
  • Tail Inequalities
  • The Probabilistic Method
  • Markov Chains and Random Walks
  • Randomized Data Structures
  • Randomized Geometric Algorithms 
  • Randomized Graph Algorithms
  • Randomized Distributed and Parallel Algorithms
Learning Outcome

Knowledge of: The relevant combinatorial probability theory and randomized techniques in algorithms (see course content).

Skills in:

 

  • proving bounds on the expected running time of randomized algorithms
  • explaining methods for bounding the probability that a random variable deviates far from its expectation
  • applying the probabilistic method to prove the existence of e.g. algorithms
  • giving algorithmic applications of random walks
  • giving simple and efficient algorithms and data structures using randomization where more traditional deterministic approaches are more cumbersome or less efficient


Competences to: Reason about and apply randomized techniques to computational problems that the student may later encounter in life.

 

Expected to be "Randomized Algorithms" by Motwani and Raghavan

Lectures and compulsory assignments
Credit
7,5 ECTS
Type of assessment
Oral examination, 30 minutter under invigilation
30 minutes preparation, 30 minutes oral examination, including grade determination.
Exam registration requirements
The student must solve mandatory exercises during the course. Assignments will be made each week and be due in the following week. 5 out of 7 assignments must be completed by the due date and approved to qualify to take the exam.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assesment

See learning outcome.

  • Category
  • Hours
  • Lectures
  • 36
  • Theory exercises
  • 84
  • Preparation
  • 85
  • Exam
  • 1
  • Total
  • 206