CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

# Algorithms and Graphs 1

The course is not on the list Without time-table
Code Completion Credits Range Language
BI-AG1 Z,ZK 6 2P+2C Czech
Garant předmětu:
Lecturer:
Tutor:
Supervisor:
Department of Theoretical Computer Science
Synopsis:

The course covers the basics of efficient algorithm design, data structures, and graph theory, belonging to the core knowledge of every computing curriculum.

It links and partially develops the knowledge from the course BI-DML.21, in which students acquire the knowledge and skills in combinatorics necessary for evaluating the time and space complexity of algorithms. The course also follows up knowledge from BI-MA1.21, the practical usage of asymptotic mathematics, in particular, the asymptotic notation.

Requirements:

Active algorithmic skills for solving basic types of computational tasks, C++ programming skills, and knowledge of basic notions from mathematical analysis and combinatorics are expected. Students should take the concurrent course BIE-AAG.21.

Syllabus of lectures:

1. Motivation and Elements of Graph Theory.

2. Basic Definitions and Elements of Graph Theory I.

3. Basic Definitions and Elements of Graph Theory II.

4. Sorting Algorithms O(n^2). Binary Heaps and HeapSort.

5. Extendable Array, Amortized Complexity, Binomial Heaps.

6. Search Trees and Balance Strategies.

7. Introduction to Randomization, Hashing.

8. Recursive algorithm and the Divide-and-Conquer method.

9. Probabilistic Algorithms and Their Complexity. QuickSort.

10. Dynamic Programming.

11. Minimum Spanning Trees.

12. Shortest Paths Algorithms on Graphs.

Syllabus of tutorials:

1. Motivation and Elements of Graph Theory I.

2. Elements of Graph Theory II.

3. Elements of Graph Theory III. 1st ProgTest.

4. Sorting Algorithms O(n^2). Binary Heaps.

5. Extendable Array, Amortized Complexity, Binomial Heaps.

6. Search Trees and Balance Strategies. 2nd ProgTest.

7. Hashing and Hash tables.

8. Recursive Algorithms and Divide et Impera Method.

9. Probabilistic Algorithms and their Complexity. QuickSort.

10. Semestral test.

11. Dynamic Programming. 3rd ProgTest.

13. Minimum Spanning Trees, Shortest Paths.

Study Objective:
Study materials:

[1] Cormen, T. H. - Leiserson, C. E. - Rivest, R. L. - Stein, C.: Introduction to Algorithms, 3rd Edition, MIT Press, 2009, 978-0262033848,

[2] Gibbons, A.: Algorithmic Graph Theory, Cambridge University Press, 1985, 978-0521288811,

[3] Gross, J. L. - Yellen, J. - Zhang, P.: Handbook of Graph Theory, 2nd Edition (Discrete Mathematics and Its Applications), Chapman and Hall/CRC, 2013, 978-1439880180,

Note:
Further information:
https://courses.fit.cvut.cz/BI-AG1/
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-06-16
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet3458106.html