CTU FEE Moodle
Theory of Algorithms
B232 - Summer 23/24
This is a grouped Moodle course. It consists of several separate courses that share learning materials, assignments, tests etc. Below you can see information about the individual courses that make up this Moodle course.
Theory of Algorithms - B4M01TAL
Main course
| Credits | 6 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 3P+2S |
Annotation
The course brings theoretical background of the theory of algorithms with the focus at first on the time and space complexity of algorithms and problems, secondly on the correctness of algorithms. Further it is dealt with the theory of complexity; the classes P, NP, NP-complete, PSPACE and NPSPACE are treated and properties of them investigated. Probabilistic algorithms are studied and the classes RP and ZZP introduced.
Study targets
No data.
Course outlines
1. Analyzing algorithms and problems, classifying functions by their growth rates, time and space complexity.
2. Correctness of algorithms, variants and invariants.
3. Decision problems and optimization problems.
4. Turing machine and its variants.
5. Relation between Turing machine and RAM machine.
6. Classes P and NP.
7. Reduction and polynomial reduction of problems.
8. NP-complete problems, Cook's Theorem.
9. Classes PSPACE and NPSPACE..
10. Randomized algorithms with polynomial time complexity.
11. Classes RP and ZZP.
12. Undecidable problems.
13. Reserve.
2. Correctness of algorithms, variants and invariants.
3. Decision problems and optimization problems.
4. Turing machine and its variants.
5. Relation between Turing machine and RAM machine.
6. Classes P and NP.
7. Reduction and polynomial reduction of problems.
8. NP-complete problems, Cook's Theorem.
9. Classes PSPACE and NPSPACE..
10. Randomized algorithms with polynomial time complexity.
11. Classes RP and ZZP.
12. Undecidable problems.
13. Reserve.
Exercises outlines
1. Determining time and space complexity of well known algorithms.
2. Verifying correctness of algorithms using variants and invariants.
3. Turing machines.
4. Polynomial reductions of problems.
5. Examples of randomized algorithms.
6. Examples of undecidable problems.
2. Verifying correctness of algorithms using variants and invariants.
3. Turing machines.
4. Polynomial reductions of problems.
5. Examples of randomized algorithms.
6. Examples of undecidable problems.
Literature
[1] Kozen, D. C.: The design and Analysis of Algorithms, Springer-Vrelag, 1991
[2] Harel, D: Algorithmics: The Spirit of Computing, Addison-Wesleyt Inc., Reading MA 2002
[3] Talbot, J., Welsh, D.: Complexity and Cryptography, Cambridge University Press, 2006
[2] Harel, D: Algorithmics: The Spirit of Computing, Addison-Wesleyt Inc., Reading MA 2002
[3] Talbot, J., Welsh, D.: Complexity and Cryptography, Cambridge University Press, 2006
Requirements
No data.
Theory of Algorithms - A4M01TAL
| Credits | 6 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 3P+1S |
Annotation
The course brings theoretical background of the theory of algorithms with the focus at first on the time and space complexity of algorithms and problems, secondly on the correctness of algorithms. Further it is dealt with the theory of complexity; the classes P, NP, NP-complete, PSPACE and NPSPACE are treated and properties of them investigated. Probabilistic algorithms are studied and the classes RP and ZZP introduced.
Study targets
No data.
Course outlines
1. Analyzing algorithms and problems, classifying functions by their growth rates, time and space complexity.
2. Correctness of algorithms, variants and invariants.
3. Decision problems and optimization problems.
4. Turing machine and its variants.
5. Relation between Turing machine and RAM machine.
6. Classes P and NP.
7. Reduction and polynomial reduction of problems.
8. NP-complete problems, Cook's Theorem.
9. Classes PSPACE and NPSPACE..
10. Randomized algorithms with polynomial time complexity.
11. Classes RP and ZZP.
12. Undecidable problems.
13. Reserve.
2. Correctness of algorithms, variants and invariants.
3. Decision problems and optimization problems.
4. Turing machine and its variants.
5. Relation between Turing machine and RAM machine.
6. Classes P and NP.
7. Reduction and polynomial reduction of problems.
8. NP-complete problems, Cook's Theorem.
9. Classes PSPACE and NPSPACE..
10. Randomized algorithms with polynomial time complexity.
11. Classes RP and ZZP.
12. Undecidable problems.
13. Reserve.
Exercises outlines
1. Determining time and space complexity of well known algorithms.
2. Verifying correctness of algorithms using variants and invariants.
3. Turing machines.
4. Polynomial reductions of problems.
5. Examples of randomized algorithms.
6. Examples of undecidable problems.
2. Verifying correctness of algorithms using variants and invariants.
3. Turing machines.
4. Polynomial reductions of problems.
5. Examples of randomized algorithms.
6. Examples of undecidable problems.
Literature
[1] Kozen, D. C.: The design and Analysis of Algorithms, Springer-Vrelag, 1991
[2] Harel, D: Algorithmics: The Spirit of Computing, Addison-Wesleyt Inc., Reading MA 2002
[3] Hopcroft, J., Motwani, R., Ullman, J.: Introduction to Automata Theory, Languages, and Computation, Addison-Wesley, 2001
[2] Harel, D: Algorithmics: The Spirit of Computing, Addison-Wesleyt Inc., Reading MA 2002
[3] Hopcroft, J., Motwani, R., Ullman, J.: Introduction to Automata Theory, Languages, and Computation, Addison-Wesley, 2001
Requirements
Logic and Graphs, Discrete Mathematics