Course - Algorithms and Data Structures - TDT4120
Algorithms and Data Structures
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About the course
Course content
Methods for analysing the efficiency of algorithms, divide and conquer techniques, recursive solution methods. Methods for ordering, searching and sorting. Data structures for efficient retrieval of data, dynamic programming and greedy algorithms. Data structures for implementing graphs and networks, as well as methods for traversals and searches. Algorithms for finding the best path(s) and matchings, spanning trees and maximum flow. Theory of problem complexity. Algorithms are expressed in a language-independent manner.
Students without access to the course may instead take TDT4121 Introduction to algorithms, which is equivalent as a basis for later courses, and is aimed toward programs that do not have computer science as part of their core.
Learning outcome
Knowledge: The candidate should have knowledge about (1) a broad spectrum of established algorithms that are useful in several areas of application, (2) classical algorithmic problems with known efficient solutions, and (3) complex problems without known efficient solutions.
Skills: The candidate should be able to (1) analyze the efficiency of an algorithm to achieve good solutions for a given problem, (2) formulate a problem so it can be handled in a rational manner by an algorithm, and (3) use well-known design methods to construct new efficient algorithms.
General competence: The candidate should be able to (1) use well-known algorithms and available program modules on new problems, and (2) develop and implement new solutions for complex problems with a basis in practical reality.
Learning methods and activities
Lectures and individual exercises.
Compulsory assignments
- Exercises
Further on evaluation
If there is a re-sit examination, the examination form may change from written to oral.
Specific conditions
Admission to a programme of study is required:
Applied Physics and Mathematics (MTFYMA)
Computer Science (MTDT)
Cyber Security and Data Communication (MTKOM)
Cybernetics and Robotics (MTTK)
Electrification and Digitalisation - Engineering (BIELDIG)
Engineering and ICT (MTING)
Industrial Economics and Technology Management (MTIØT)
Informatics (BIT)
Natural Science with Teacher Education, years 8 - 13 (MLREAL)
Recommended previous knowledge
The students are assumed to have basic programming skills, and to have a good understanding of recursion. The students are also assumed to be familiar with basic mathematical notation and such concepts as functions, monotonicity, logarithms, polynomials, limits, sets, relations, orders, graphs, trees, permutations and combinations, proof by induction, series and partial sums, and basic probability calculus.
Course materials
Cormen, Leiserson, Rivest, Stein: Introduction to Algorithms, fourth edition. (This may change.)
Credit reductions
| Course code | Reduction | From |
|---|---|---|
| SIF8010 | 7.5 sp | |
| IT1105 | 7.5 sp | |
| MNFIT115 | 7.5 sp | |
| MNFIT112 | 7.5 sp | |
| IDATA2302 | 7.5 sp | Autumn 2020 |
| IDATT2101 | 7.5 sp | |
| TDT4121 | 7.5 sp | Autumn 2022 |
Subject areas
- Technological subjects