Syllabus for Algorithms and Data Structures III

Learning outcomes

On completion of the course, the student should be able to:

• analyse NP-completeness of an algorithmic problem;
• use advanced algorithm analysis methods, such as amortised analysis and probabilistic analysis;
• use advanced algorithm design methods in order to approach hard algorithmic problems in a pragmatic way, such as by using randomised algorithms (such as universal hashing), approximation algorithms, stochastic local search (such as simulated annealing and tabu search), integer programming, propositional satisfiability (SAT), and SAT modulo theories (SMT).
• present and discuss topics related to the course content orally and in writing with a skill appropriate for the level of education.

Content

NP-completeness. Advanced techniques in algorithm analysis and design such as amortised and probabilistic analysis, universal hashing, integer programming, simulated annealing, tabu search, propositional satisfiability (SAT). Connections to modern research in algorithmics.

Instruction

Lectures, lessons and exercises.

Assessment

Written and oral presentation of assignments and written and oral exam.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Transitional provisions

The course cannot be included in a degree together with the courses Algorithms and data structures (DV)3/III (1DL104, 1DL113, 1DL030) and Advanced algorithmics (1DL480).