Syllabus for Algorithms and Data Structures III

Algoritmer och datastrukturer III

A revised version of the syllabus is available.

  • 5 credits
  • Course code: 1DL481
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2015-03-12
  • Established by:
  • Revised: 2018-08-30
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Spring 2019
  • Entry requirements:

    120 credits including 30 credits in mathematics and 45 credits in computer science. Participation in Algorithms and Data Structures II. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Information Technology

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).

Content

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

Instruction

Lectures, lessons and exercises.

Assessment

Written and oral presentation of assignments, 2 credits, and written and oral exam, 3 credits.

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).

Reading list

The reading list is missing. For further information, please contact the responsible department.