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Fundamentals of the Analysis of Algorithm Efficiency
Introduction ,[object Object],[object Object],[object Object],[object Object]
Order of Growth ,[object Object],[object Object],[object Object],[object Object]
Worst-Case, Best-Case & Average-Case Efficiencies ,[object Object],[object Object],[object Object],[object Object]
Sequential Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sequential Search (contd.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sequential Search (contd.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Asymptotic Notations ,[object Object],[object Object],[object Object]
O-notation  (Big Oh) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ω -notation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Θ -notation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mappings for n 2 Ω (n 2  ) O(n 2  ) Θ ( n 2 )
Bounds of a Function
Examples of algorithms for sorting techniques and their complexities ,[object Object],[object Object],[object Object],[object Object]
 
 
 
 
Time Efficiency Analysis ,[object Object],[object Object],[object Object],[object Object],=(n-1) 2  - (n-2)(n-1)/2 = (n-1)n/2 ≈  ½ n 2  Є Θ (n 2 )
Recurrences ,[object Object],[object Object],[object Object]
Mathematical Analysis of Recursive Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example 1 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example 1 (contd.) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example 2
Example 2 (contd.) ,[object Object]
Example 3 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example 3 (contd.) ,[object Object],[object Object],[object Object],Whose solution is claimed to be T(n)= Θ(n lg n). The equation 1.0 can be rewritten as follows: T(n)=   Where the constant c represents the time required to  solve problems of size 1 as well as the time per array  element of the divide and combine steps.
Example 3 (contd.) cn T(n) cn T(n/2) T(n/2 ) cn/2 cn/2 T(n/4) T(n/4) T(n/4) T(n/4) (a) (b) (c)
Example 3 (contd.) ,[object Object],[object Object],[object Object]
Example 3 (contd.) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Randomized Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object]
Algorithm Visualization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Assignment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Thank You!

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Slide2