Algorithms and Graphs No Implementation
Code  Completion  Credits  Range  Language 

BIEAX1  Z,ZK  4  2P+2C  English 
 Vztahy:
 It is not possible to register for the course BIEAX1 if the student is concurrently registered for or has already completed the course BIEAG1.21 (mutually exclusive courses).
 It is not possible to register for the course BIEAX1 if the student is concurrently registered for or has previously completed the course BIEAG1 (mutually exclusive courses).
 It is not possible to register for the course BIEAX1 if the student is concurrently registered for or has previously completed the course BIEAG1.21 (mutually exclusive courses).
 Garant předmětu:
 Dušan Knop
 Lecturer:
 Tomáš Valla
 Tutor:
 Dušan Knop, Maria Saumell Mendiola, Jiřina Scholtzová
 Supervisor:
 Department of Theoretical Computer Science
 Synopsis:

The course covers the basics from the efficient algorithm design, data structures, and graph theory, belonging to the core knowledge of every computing curriculum. It is interlinked with the concurrent BIEAAG and BIEZDM courses in which the students gain the basic skills and knowledge needed for time and space complexity of algorithms and learn to handle practically the asymptotic mathematics.
 Requirements:

basics of combinatorics and discrete math, basics of logic
 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 DivideandConquer 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, 2016. ISBN 9780262033848.
2. Wengrow J. : A CommonSense Guide to Data Structures and Algorithms: Level Up Your Core Programming Skills (2nd Edition). Pragmatic Bookshelf, 2020. ISBN 9781680507225.
3. Sedgewick R. : Algorithms (4th Edition). AddisonWesley, 2011. ISBN 9780321573513.
4. Deo N. : Graph Theory with Applications to Engineering and Computer Science. Dover Publications, 2016. ISBN 978048680793.
5. Bickle A. : Fundamentals of Graph Theory. AMS, 2020. ISBN 9781470453428.
 Note:
 Further information:
 not filled in
 Timetable for winter semester 2024/2025:
 Timetable is not available yet
 Timetable for summer semester 2024/2025:
 Timetable is not available yet
 The course is a part of the following study plans: