Algorithms and Graphs 1
Code  Completion  Credits  Range  Language 

BIAG1  Z,ZK  6  2P+2C  Czech 
 Garant předmětu:
 Dušan Knop
 Lecturer:
 Dušan Knop, Tomáš Valla
 Tutor:
 Michal Dvořák, Radek Hušek, Dušan Knop, Jan Matyáš Křišťan, Xuan Thang Nguyen, Jan Pokorný, Šimon Schierreich, Martin Slávik, Ondřej Šofr, Tomáš Valla
 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:

Active algorithmic skills for solving basic types of computational tasks, programming skills in some HLL (Java, C++), and knowledge of basic notions from the mathematical analysis and combinatorics are expected. Students are expected to take concurrent courses BIEAAG and BIEZDM.
 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.W
 Study Objective:
 Study materials:

[1] Cormen, T. H.  Leiserson, C. E.  Rivest, R. L.  Stein, C.: Introduction to Algorithms, 3rd Edition, MIT Press, 2009, 9780262033848,
[2] Gibbons, A.: Algorithmic Graph Theory, Cambridge University Press, 1985, 9780521288811,
[3] Gross, J. L.  Yellen, J.  Zhang, P.: Handbook of Graph Theory, 2nd Edition (Discrete Mathematics and Its Applications), Chapman and Hall/CRC, 2013, 9781439880180,
 Note:
 Further information:
 https://courses.fit.cvut.cz/BIAG1/
 No timetable has been prepared for this course
 The course is a part of the following study plans:

 Bachelor program Informatics, unspecified branch, in Czech, 20152020 (compulsory course in the program)
 Bachelor branch Security and Information Technology, in Czech, 20152020 (compulsory course in the program)
 Bachelor branch Computer Science, in Czech, 20152020 (compulsory course in the program)
 Bachelor branch Computer Engineering, in Czech, 20152020 (compulsory course in the program)
 Bachelor branch Information Systems and Management, in Czech, 20152020 (compulsory course in the program)
 Bachelor branch Knowledge Engineering, in Czech, 20152017 (compulsory course in the program)
 Bachelor branch Web and Software Engineering, spec. Software Engineering, in Czech, 20152020 (compulsory course in the program)
 Bachelor branch Web and Software Engineering, spec. Web Engineering, in Czech, 20152020 (compulsory course in the program)
 Bachelor branch Web and Software Engineering, spec. Computer Graphics, in Czech, 20152020 (compulsory course in the program)
 Bachelor branch Knowledge Engineering, in Czech, 20182020 (compulsory course in the program)
 Bachelor branch Web and Software Engineering, spec. Computer Graphics, in Czech, Dubin (compulsory course in the program)