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Chapter 4

Chapter 4. Analysis Tools. Why we Analyze Algorithms. We try to save computer resources such as : Memory space used to store data structures, Space Complexity CPU time , which is reflected by Algorithm Run Time Complexity. 4.1 Seven Functions. The Constant Function: f(n)= c,

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Chapter 4

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  1. Chapter 4 Analysis Tools Algorithm Analysis

  2. Algorithm Analysis

  3. Why we Analyze Algorithms We try to save computer resources such as: • Memory space used to store data structures, Space Complexity • CPU time, which is reflected by Algorithm Run Time Complexity. Algorithm Analysis

  4. 4.1 Seven Functions • The Constant Function:f(n)= c, wherec is some fixed constant, c=5, c=100, or c=210, so we let g(n)=1, so f(n)=cg(n) • The Logarith Function:f(n)= logbn x = logbn iff bx=n , (see Proposition 4.1), the most common base for logarithmic function in computer science is 2. • Linear Function:f(n)= n , important for algorithms on vectors (one dim. Arrays). Algorithm Analysis

  5. The N-Log-N Function:f(n)= nlogn This function grows a little faster than linear function and a lot slower than quadratic function. • The Quadratic Function:f(n)= n2 It’s important for algorithms of nested loops. • The Cubic Function (Polynomial): f(n)= n3 f(n)=a0+a1n+ a2n2+…+adnd, is a polynomial of degree d, where a0 , a1 ,…., ad are constants. • The Exponential Function:f(n)= bn Algorithm Analysis

  6. Algorithm Analysis

  7. Algorithm Analysis

  8. Algorithm Analysis

  9. Algorithm Analysis

  10. Algorithm Analysis

  11. Algorithm Analysis

  12. Algorithm Analysis

  13. Algorithm Analysis

  14. We define a set of primitive operations such as the following: • Assigning a value to a variable • Calling a method • Performing an arithmetic operation (for example, adding two numbers) • Comparing two numbers • Indexing into an array • Following an object reference • Returning from a method. Algorithm Analysis

  15. Algorithm Analysis

  16. Algorithm Analysis

  17. Primitive Operations Algorithm Analysis

  18. Counting Primitive Operations Algorithm Analysis

  19. Algorithm Analysis

  20. Algorithm Analysis

  21. Algorithm Analysis

  22. 4.2.3 Asymptotic Notation Algorithm Analysis

  23. Example Algorithm Analysis

  24. Algorithm Analysis

  25. Algorithm Analysis

  26. Properties of Big-Oh Notation Algorithm Analysis

  27. Algorithm Analysis

  28. Algorithm Analysis

  29. Algorithm Analysis

  30. Algorithm Analysis

  31. Algorithm Analysis

  32. Algorithm Analysis

  33. Big-Omega and Big-Theta Algorithm Analysis

  34. Algorithm Analysis

  35. Big-Omega • Just as Big-Oh notation provides us a way of saying that a function f(n) is “less than or equal to” another function, • Big-Omega notation provides us a way of saying that a function f(n) is “greater than or equal to” another function. • Let f(n) and g(n) be functions mapping non-negative integers: We say that f(n) is Ω(g(n)) if g(n) is O(f(n)) • That’s there is a real constant c>0 and an integer constant n0 ≥ 1 such that: f(n) ≥ cg(n), for n ≥ n0 Algorithm Analysis

  36. Big-Theta • In addition, there is a notation that allows us to say that two functions grow at the same rate , up to a constant factor. • We say that f(n) is Θ(g(n)) if f(n) is O(g(n)) and f(n) is Ω(g(n)), • That’s, there are real constants c’>0 and c”>0 and an integer constant n0 ≥ 1 such that: c’g(n) ≤ f(n) ≥ c”g(n), for n ≥ n0 . Algorithm Analysis

  37. Example • f(n) = 3nlog n + 4n + 5log n isΘ(nlog n) • Since 3nlog n + 4n + 5log n ≥ 3nlog n for n ≥2 • So, f(n) is Ω(nlog n) • Since 3nlog n + 4n + 5log n ≤ (3+4+5)nlog n for n ≥2 • So, f(n) is O(nlog n) • Thus, f(n) is Θ(nlog n) Algorithm Analysis

  38. Algorithm Analysis

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