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Algorithms Slides Emanuele Viola 2009 present Released under - PowerPoint PPT Presentation

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Algorithms Slides Emanuele Viola 2009 present Released under Creative Commons License Attribution -Noncommercial-No Derivative Works 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ Also, let me know if you use

  1. Algorithms Slides Emanuele Viola 2009 – present Released under Creative Commons License “Attribution -Noncommercial-No Derivative Works 3.0 United States” http://creativecommons.org/licenses/by-nc-nd/3.0/us/ Also, let me know if you use them.

  2. Index The slides are under construction. The latest version is at http://www.ccs.neu.edu/home/viola/

  4. Success stories of algorithms: Shortest path (Google maps) Pattern matching (Text editors, genome) Fast-fourier transform (Audio/video processing) http://cstheory.stackexchange.com/questions/19759/core-algorithms-deployed

  5. This class: General techniques: Divide-and-conquer, dynamic programming, data structures amortized analysis Various topics: Sorting Matrixes Graphs Polynomials

  6. What is an algorithm? ● Informally, an algorithm for a function f : A → B (theproblem) is a simple, step-by-step, procedure that computes f(x) on every input x

  7. What operations are simple? ● If, for, while, etc. ● Direct addressing: A[n], the n-entry of arrayA ● Basic arithmetic and logic on variables x * y, x + y, x AND y, etc. – – Simple in practice only if the variables are “small”. For example, 64 bits on current PC – Sometimes we get cleaner analysis if we consider them simple regardless of size of variables.

  8. Measuring performance ● We bound the running time, or the memory (space) used. ● These are measured as a function of the input length. ● Makes sense: need to at least read the input! ● The input length is usually denoted n ● We are interested in which functions of n grow faster

  9. 2 n n 1.5 n 2 n log 2 (n) n log(n) n

  10. Asymptotic analysis ● The exact time depends on the actual machine ● We ignore constant factors, to have more robust theory that applies to most computer ● Example: on my computer it takes 67 n + 15 operations, on yours 58 n – 15, but that's about the same ● We now give definitions that make this precise

  11. Big-Oh Definition: f(n) = O(g(n)) if there are ( ∃ ) constants c, n 0 such that f(n) ≤ c ∙g(n), for every ( ∀ ) n ≥ n 0 . Meaning: f grows no faster than g, up to constant factors

  12. Big-Oh Definition: f(n) = O(g(n)) if there are ( ∃ ) constants c, n 0 such that f(n) ≤ c ∙g(n), for every ( ∀ ) n ≥ n 0 . Example 1: 5n + 2n 2 + log(n) = O(n 2 ) ?

  13. Big-Oh Definition: f(n) = O(g(n)) if there are ( ∃ ) constants c, n 0 such that f(n) ≤ c ∙g(n), for every ( ∀ ) n ≥ n 0 . Example 1: 5n + 2n 2 + log(n) = O(n 2 ) True Pick c = ?

  14. Big-Oh Definition: f(n) = O(g(n)) if there are ( ∃ ) constants c, n 0 such that f(n) ≤ c ∙g(n), for every ( ∀ ) n ≥ n 0 . Example 1: 5n + 2n 2 + log(n) = O(n 2 ) True Pick c = 3. For large enough n, 5n + log(n) ≤ n 2 . Any c > 2 would work.

  15. Example 2: 100n 2 = O(2 n ) ?

  16. Example 2: 100n 2 = O(2 n ) True Pick c = ?

  17. Example 2: 100n 2 = O(2 n ) True Pick c = 1. Any c > 0 would work, for large enough n.

  18. Example 3: n 2 log n = O(n 2 ) ?

  19. Example 3: n 2 log n ≠ O(n 2 )  c, n 0  n ≥ n 0 such that n 2 log n > c n 2 . n > 2 c ⇨ n 2 log n > n 2 c

  20. Example 4: 2 n = O(2 n/2 ) ?

  21. Example 4: 2 n ≠ O(2 n/2 ).  c, n 0  n ≥ n 0 such that 2 n > c∙2 n/2 . Pick any n > 2 log c 2 n = 2 n/2 2 n/2 > c∙2 n/2 .

  22. ● n log n = O(n 2 ) ? n 2 = O(n 1.5 log10n) ? ● 2 n = O(n 1000000 )? ● (√2) log n = O(n 1/3 ) ? ● n log log n = O((log n) log n )? ● 2 n = O(4 log n )? ● n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  23. ● n log n = O(n 2 ). n 2 = O(n 1.5 log10n) ? ● 2 n = O(n 1000000 )? ● (√2) log n = O(n 1/3 ) ? ● n log log n = O((log n) log n )? ● 2 n = O(4 log n )? ● n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  24. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n = O(n 1000000 )? ● (√2) log n = O(n 1/3 ) ? ● n log log n = O((log n) log n )? ● 2 n = O(4 log n )? ● n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  25. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ) ● (√2) log n = O(n 1/3 ) ? ● n log log n = O((log n) log n )? ● 2 n = O(4 log n )? ● n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  26. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n = O(n 1/3 ) ? (√2) log n = n 1/2 ≠ O(n 1/3 ) ● n log log n = O((log n) log n )? ● 2 n = O(4 log n ) ? ● n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  27. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n ≠ O(n 1/3 ). ● n log log n = O((log n) log n )? ● 2 n = O(4 log n )? ● n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  28. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n ≠ O(n 1/3 ). ● n log log n = n log log n = O((log n) log n )? ● 2 logn. log log n = 2 n = O(4 log n )? ● (log n) log n . n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  29. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n ≠ O(n 1/3 ). ● n log log n = O((log n) log n ). ● 2 n = O(4 log n )? ● n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  30. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n ≠ O(n 1/3 ). ● n log log n = O((log n) log n ). ● log n ● 2 n = O(4 log n ) ? 4 log n =2 2log n 2 n =2 2 . n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  31. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n ≠ O(n 1/3 ). ● n log log n = O((log n) log n ). ● 2 n ≠ O(4 logn ). ● n! = O(2 n ) ? ● n! = O(n n ) ? ● n2 n = O(2 n log n ) ? ●

  32. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n ≠ O(n 1/3 ). ● n log log n = O((log n) log n ). ● 2 n ≠ O(4 logn ). ● n! ≠ O(2 n ). 2.5 √n (n/e) n ≤ n! ≤ 2.8 √n (n/e) n ● n! = O(n n ) ? ● ● n2 n = O(2 n log n ) ?

  33. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n ≠ O(n 1/3 ). ● n log log n = O((log n) log n ). ● 2 n ≠ O(4 logn ). ● n! ≠ O(2 n ). ● n! = O(n n ). ● n2 n = O(2 n log n ) ? ●

  34. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n ≠ O(n 1/3 ). ● n log log n = O((log n) log n ). ● 2 n ≠ O(4 logn ). ● n! ≠ O(2 n ). ● n! = O(n n ). ● n2 n = O(2 n log n ) ? n2 n = 2 log n+n . ●

  35. ● n log n = O(n 2 ). n 2 ≠ O(n 1.5 log10n). ● 2 n ≠ O(n 1000000 ). ● (√2) log n ≠ O(n 1/3 ). ● n log log n = O((log n) log n ). ● 2 n ≠ O(4 logn ). ● n! ≠ O(2 n ). ● n! = O(n n ). ● n2 n = O(2 n log n ). ●

  36. Big-omega Definition: f(n) = Ω (g(n)) means  c, n 0 > 0  n ≥ n 0 , f(n) ≥ c ∙g(n). Meaning: f grows no slower than g, up to constant factors

  37. Big-omega Definition: f(n) = Ω (g(n)) means  c, n 0 > 0  n ≥ n 0 , f(n) ≥ c ∙g(n). Example 1: 0.01 n = Ω (log n) ?

  38. Big-omega Definition: f(n) = Ω (g(n)) means  c, n 0 > 0  n ≥ n 0 , f(n) ≥ c ∙g(n). Example 1: 0.01 n = Ω (log n) True Pick c = 1. Any c > 0 would work

  39. Example 2: n 2 /100 = Ω (n log n)?

  40. Example 2: n 2 /100 = Ω (n log n). c = 1/100 Again, any c would work.

  41. Example 2: n 2 /100 = Ω (n log n). c = 1/100 Again, any c would work. Example 3: √n = Ω(n/100) ?

  42. Example 2: n 2 /100 = Ω ( n log n). c = 1/100 Again, any c would work. Example 3: √n ≠ Ω(n/100)  c, n 0  n ≥ n 0 such that , √ n < c∙ n/100.

  43. Example 4: 2 n/2 = Ω(2 n ) ?

  44. Example 4: 2 n/2 ≠ Ω(2 n )  c, n 0  n ≥ n 0 such that 2 n/2 < c∙2 n .

  45. Big-omega, Big-Oh Note: f(n) = Ω (g(n)) ⇔ g(n) = O( f(n)) f(n) = O (g(n)) ⇔ g(n) = Ω ( f(n)). Example: 10 log n = O(n), and n = Ω (10 log n). 5n = O(n), and n = Ω(5n)

  46. Theta Definition: f(n) = Θ (g(n)) means  n 0 , c 1 , c 2 > 0  n ≥ n 0 , f(n) ≤ c 1 ∙g(n) and g(n) ≤ c 2 ∙f(n). Meaning: f grows like g, up to constant factors

  47. Theta Definition: f(n) = Θ (g(n)) means  n 0 , c 1 , c 2 > 0  n ≥ n 0 , f(n) ≤ c 1 ∙g(n) and g(n) ≤ c 2 ∙f(n). Example: n = Θ (n + log n) ?

  48. Theta Definition: f(n) = Θ (g(n)) means  n 0 , c 1 , c 2 > 0  n ≥ n 0 , f(n) ≤ c 1 ∙g(n) and g(n) ≤ c 2 ∙f(n). Example: n = Θ (n + log n) True c 1 = ?, c 2 = ? n 0 = ? such that  n ≥ n 0 , n ≤ c 1 (n + log n) and n + log n ≤ c 2 n.

  49. Theta Definition: f(n) = Θ (g(n)) means  n 0 , c 1 , c 2 > 0  n ≥ n 0 , f(n) ≤ c 1 ∙g(n) and g(n) ≤ c 2 ∙f(n). Example: n = Θ (n + log n) True c 1 = 1, c 2 = 2 n 0 = 2 such that  n ≥ 2, n ≤ 1 (n + log n) and n + log n ≤ 2 n.

  50. Theta Definition: f(n) = Θ (g(n)) means  n 0 , c 1 , c 2 > 0  n ≥ n 0 , f(n) ≤ c 1 ∙g(n) and g(n) ≤ c 2 ∙f(n). Note: f(n) = Θ (g(n)) ⇔ f(n) = Ω (g(n)) and f(n) = O(g(n)) f(n) = Θ (g(n)) ⇔ g(n)= Θ (f(n)).

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