Estimate in terms of n, the worst case time complexity of each of the following pseudo-code snippets:

Computer Networking: A Top-Down Approach (7th Edition)
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Author:James Kurose, Keith Ross
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Chapter1: Computer Networks And The Internet
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1. Estimate in terms of n, the worst case time complexity of each of the following
pseudo-code snippets:
a.
ALGORITHM sum(n)
sum -0
for i 0 to n do
for j +0 to n*n do
sum Esum + 1
return sum
Transcribed Image Text:1. Estimate in terms of n, the worst case time complexity of each of the following pseudo-code snippets: a. ALGORITHM sum(n) sum -0 for i 0 to n do for j +0 to n*n do sum Esum + 1 return sum
b.
ALGORITHM Unique Elements(A[0..n – 1])
|
//Determines whether all the elements in a given array are distinct
/Input: An array A[0..n – 1]
//Output: Returns "true" if all the elements in A are distinct
//
and "false" otherwise
for i 0 to n - 2 do
for j i+1 to n -1 do
if A[i]= A[j]return false
return true
Transcribed Image Text:b. ALGORITHM Unique Elements(A[0..n – 1]) | //Determines whether all the elements in a given array are distinct /Input: An array A[0..n – 1] //Output: Returns "true" if all the elements in A are distinct // and "false" otherwise for i 0 to n - 2 do for j i+1 to n -1 do if A[i]= A[j]return false return true
Expert Solution
Basics
  • An algorithm is an arrangement of unambiguous guidelines for settling a issue, i.e., for acquiring a necessary yield for any authentic contribution to a limited measure of time.
  • Time complexity shows how quick a calculation runs.
  • It is usually assessed by tallying the quantity of rudimentary tasks performed by the calculation, if each rudimentary activity sets aside a fixed measure of effort to perform.
  • They are ordered by the sort of capacity showing up in the big O documentation.
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