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Time Complexity

Time Complexity. Consider a deterministic Turing Machine which decides a language . For any string the computation of terminates in a finite amount of transitions. Initial state. Accept or Reject. Decision Time = #transitions. Initial state. Accept or Reject.

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Time Complexity

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  1. Time Complexity Prof. Busch - LSU

  2. Consider a deterministic Turing Machine which decides a language Prof. Busch - LSU

  3. For any string the computation of terminates in a finite amount of transitions Initial state Accept or Reject Prof. Busch - LSU

  4. Decision Time = #transitions Initial state Accept or Reject Prof. Busch - LSU

  5. Consider now all strings of length = maximum time required to decide any string of length Prof. Busch - LSU

  6. TIME STRING LENGTH Max time to accept a string of length Prof. Busch - LSU

  7. Time Complexity Class: All Languages decidable by a deterministic Turing Machine in time Prof. Busch - LSU

  8. Example: This can be decided in time Prof. Busch - LSU

  9. Other example problems in the same class Prof. Busch - LSU

  10. Examples in class: Prof. Busch - LSU

  11. Examples in class: CYK algorithm Matrix multiplication Prof. Busch - LSU

  12. Polynomial time algorithms: constant Represents tractable algorithms: for small we can decide the result fast Prof. Busch - LSU

  13. It can be shown: Prof. Busch - LSU

  14. The Time Complexity Class Represents: • polynomial time algorithms • “tractable” problems Prof. Busch - LSU

  15. Class CYK-algorithm Matrix multiplication Prof. Busch - LSU

  16. Exponential time algorithms: Represent intractable algorithms: Some problem instances may take centuries to solve Prof. Busch - LSU

  17. Example: the Hamiltonian Path Problem s t Question: is there a Hamiltonian path from s to t? Prof. Busch - LSU

  18. s t YES! Prof. Busch - LSU

  19. A solution: search exhaustively all paths L = {<G,s,t>: there is a Hamiltonian path in G from s to t} Exponential time Intractable problem Prof. Busch - LSU

  20. The clique problem Does there exist a clique of size 5? Prof. Busch - LSU

  21. The clique problem Does there exist a clique of size 5? Prof. Busch - LSU

  22. Example: The Satisfiability Problem Boolean expressions in Conjunctive Normal Form: clauses Variables Question: is the expression satisfiable? Prof. Busch - LSU

  23. Example: Satisfiable: Prof. Busch - LSU

  24. Example: Not satisfiable Prof. Busch - LSU

  25. exponential Algorithm: search exhaustively all the possible binary values of the variables Prof. Busch - LSU

  26. Non-Determinism Language class: A Non-Deterministic Turing Machine decides each string of length in time Prof. Busch - LSU

  27. Non-Deterministic Polynomial time algorithms: Prof. Busch - LSU

  28. The class Non-Deterministic Polynomial time Prof. Busch - LSU

  29. The satisfiability problem Example: Non-Deterministic algorithm: • Guess an assignment of the variables • Check if this is a satisfying assignment Prof. Busch - LSU

  30. Time for variables: • Guess an assignment of the variables • Check if this is a satisfying assignment Total time: Prof. Busch - LSU

  31. The satisfiability problem is an - Problem Prof. Busch - LSU

  32. Observation: Deterministic Polynomial Non-Deterministic Polynomial Prof. Busch - LSU

  33. Open Problem: WE DO NOT KNOW THE ANSWER Prof. Busch - LSU

  34. Open Problem: Example: Does the Satisfiability problem have a polynomial time deterministic algorithm? WE DO NOT KNOW THE ANSWER Prof. Busch - LSU

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