Algorithm Complexity presentation slides. Time Complexity and Space Complexity analysis using Big-O notation with examples that demonstrates how a function complexity effects to algorithm efficiency.

1. Algorithm Complexity
&
Big-O Analysis

2. What is good code?

3. What is good code?
1. Readable

4. What is good code?
1. Readable
2. Scalable

5. What is good code?
1. Readable
2. Scalable
• Speed
• Memory

6. What is good code?
1. Readable
2. Scalable (Big-O)
• Speed
• Memory

7. What is good code?
(Big-O)

8. Big-O Notation

9. Big-O Notation

10. Big-O Notation
myValue = “Hello world” // O(1)

11. Big-O Notation
myValue = “Hello world” // O(1)
i input.length
myValue i // O(n)

12. Complexity

13. Complexity

14. Space Complexity

15. Space Complexity

16. Space Complexity

17. Space Complexity

18. Space Complexity

19. Space Complexity
O(2n + 13)

20. Space Complexity
O(n)

21. Space Complexity

22. Complexity

23. Complexity

24. Time Complexity

25. Time Complexity

26. Time Complexity

27. Time Complexity

28. Time Complexity

29. Time Complexity
O(4n + 3)

30. Time Complexity
O(n)

31. Time Complexity
Constant Time: O(1)
def
return

32. Time Complexity
Logarithmic Time: O(log n)
def
return

33. Time Complexity
Linear Time: O(n)
def
return

34. Time Complexity
Quadratic Time: O(n^2)
def
return result

35. Big-O Complexity Chart
Big-O Cheat Sheet

36. Linear Quadratic

37. Linear Quadratic

38. Linear Quadratic

39. Linear Quadratic

40. Linear Quadratic

41. Linear Quadratic

42. Example: Find Matching Pair
Problem:

43. Example: Find Matching Pair
8
[1,2,3,9]

44. Example: Find Matching Pair
8
[1,2,3,9] False

45. Example: Find Matching Pair
8
[1,2,3,9] False
[1,2,4,4]

46. Example: Find Matching Pair
8
[1,2,3,9] False
[1,2,4,4] True

47. Example: Find Matching Pair
8
[1,2,3,9] False
[1,2,4,4] True

48. Example: Find Matching Pair
8
[1,2,3,9] False
[1,2,4,4] True

49. Scalability Matters

50. Scalability Matters

51. Scalability Matters

52. Time Complexity

53. Read the documentation

54. Read the documentation
a

55. Read the documentation

56. Read the documentation
.count == // O(n)

57. Read the documentation
.isEmpty // O(1)

58. *Prefer HashSet instead of Array
HashSet/Dictionary Array/List

59. *Prefer HashSet instead of Array
HashSet/Dictionary Array/List
: Set< > = [1, 2, 3, 4, 5]
: Array< > = [1, 2, 3, 4, 5]

60. *Prefer HashSet instead of Array
HashSet/Dictionary Array/List
: Set< > = [1, 2, 3, 4, 5]
: Array< > = [1, 2, 3, 4, 5]

61. *Prefer HashSet instead of Array
HashSet/Dictionary Array/List
: Set< > = [1, 2, 3, 4, 5]
: Array< > = [1, 2, 3, 4, 5]
O(1)

62. Memoization

63. Memoization

64. Memoization

65. Memoization
32 .

66. Memoization

67. Memoization

68. Memoization
??

69. Memoization
0,08

70. Memoization
0,08
32
~400000 times faster ✅

71. Practice on HackerRank, LeetCode etc..

72. References
https://developer.apple.com/videos/play/wwdc2018/223
https://www.freecodecamp.org/news/big-o-notation-why-it-matters-and-why-it-doesnt-1674cfa8a23c
Introduction to Algorithms - MIT OpenCourseWare

73. Thank You