CTU FEE Moodle
Advanced algorithms
B232 - Summer 23/24
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Advanced algorithms - B4M33PAL
| Credits | 6 |
| Semesters | Winter |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 2P+2C |
Annotation
Basic graph algorithms and graph representation. Combinatorial algorithms. Application of formal languages theory in computer science - pattern matching.
Study targets
Fundamental overview and skills related to the topics of the course.
Course outlines
Formal and informal analysis of the memory and time complexity of all data sructures and algorithms taught is an integral part of the course, it is not expicitely listed under particular topics.
1. Asymptotic complexity of algorithms. Graphs, their properties and memory representation.
2. Minimum spanning tree. Union-Find problem.
3. Euler paths. Directed graphs: connectivity, acyclic graphs.
4. Heaps. Fibonacci heap. Heaps performance comparison.
5. Dynamic data structures. Garbage collector.
6. Generating, enumeration aand isomorphism of data structures and combinatorial objects. Permutations, combinations, variations, trees.
7. Generating other combinatorial structures: k-element subsets, Gray code, non-isomorphic graphs.
8. Search in sequences - linear and quadratic interpolation. Median search.
9. Finite automata, implementation, automaton reduction.
10. Regular expressions and text search using regular expressions.
11. Approximate text search using finite automata, dictionary automata.
12. Search in higher dimensions, K-D trees, Quadtree.
13. Search trees: B a B+; 2-3-4 a R-B trees.
14. Search trees: Trie, suffix tree, splay tree.
1. Asymptotic complexity of algorithms. Graphs, their properties and memory representation.
2. Minimum spanning tree. Union-Find problem.
3. Euler paths. Directed graphs: connectivity, acyclic graphs.
4. Heaps. Fibonacci heap. Heaps performance comparison.
5. Dynamic data structures. Garbage collector.
6. Generating, enumeration aand isomorphism of data structures and combinatorial objects. Permutations, combinations, variations, trees.
7. Generating other combinatorial structures: k-element subsets, Gray code, non-isomorphic graphs.
8. Search in sequences - linear and quadratic interpolation. Median search.
9. Finite automata, implementation, automaton reduction.
10. Regular expressions and text search using regular expressions.
11. Approximate text search using finite automata, dictionary automata.
12. Search in higher dimensions, K-D trees, Quadtree.
13. Search trees: B a B+; 2-3-4 a R-B trees.
14. Search trees: Trie, suffix tree, splay tree.
Exercises outlines
Exercises and related homeworks are devoted mostly to implementation of lecture topics. Consequently, the themes of each exercise formally correspond to those of respective lecture.
Literature
R. Sedgewick: Algoritmy v C, SoftPress 2003,
T. H. Cormen, C. E. Leiserson, R. L. Rievest, C. Stein: Introduction to Algorithms, 2nd ed., MIT Press, 2001
B. Melichar: Jazyky a překlady, Praha , ČVUT 1996
J. E. Hopcroft, R. Motwani, J. D. Ullman: Introduction to Automata Theory, Languages, and Computation, 2nd ed., Addison-Wesley, 2001
T. H. Cormen, C. E. Leiserson, R. L. Rievest, C. Stein: Introduction to Algorithms, 2nd ed., MIT Press, 2001
B. Melichar: Jazyky a překlady, Praha , ČVUT 1996
J. E. Hopcroft, R. Motwani, J. D. Ullman: Introduction to Automata Theory, Languages, and Computation, 2nd ed., Addison-Wesley, 2001
Requirements
Individual implementation of data types and algorithms discussed in the lectures is an important part of the exercises. Thus, capabilty of programmatic manipulation of linked data structures in some of the prevalent languages (C/C++/Java/...) is indispensable.