What is the time complexity of the below function?ALGORITHM BruteForceClosestPair(P)/Finds distance between two closest points in the plane by brute force/Input: A list P of n (n> 2) points p¡(x1, y1), . .. , Pn (Xp» Yn)//Output: The distance between the closest pair of pointsd + 00for i +1 to n – 1 dofor j ei +1 to n dod < min(d, sqrt((x; – x;)² + (y¡ – y;)²)) llsqrt is square rootreturn dO(n(log n)²)O(n²)O (n)O(n log n)

In brute force approach we consider all the possible situation/condition that can occur.…

ALGORITHM BruteForceClosestPair(P) //Finds distance between two closest points in the plane by brute force //Input: A list P of n (n > 2) points p;(x1, Yj), . . . , Pn(Xp: Yn) //Output: The distance between the closest pair of points d + 00 for i +1 to n – 1 do for jei+1 to n do d < min(d, sqrt((x; – xj)² + (y¡ – y;)²)) llsqrt is square root return d O(n(log n)?) O(n²) O(n) O(n log n)

ALGORITHM BruteForceClosestPair(P) //Finds distance between two closest points in the plane by brute force //Input: A list P of n (n > 2) points p;(x1, Yj), . . . , Pn(Xp: Yn) //Output: The distance between the closest pair of points d + 00 for i +1 to n – 1 do for jei+1 to n do d < min(d, sqrt((x; – xj)² + (y¡ – y;)²)) llsqrt is square root return d O(n(log n)?) O(n²) O(n) O(n log n)

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Question

What is the time complexity of the below function?
ALGORITHM BruteForceClosestPair(P)
/Finds distance between two closest points in the plane by brute force
/Input: A list P of n (n> 2) points p¡(x1, y1), . .. , Pn (Xp» Yn)
//Output: The distance between the closest pair of points
d + 00
for i +1 to n – 1 do
for j ei +1 to n do
d < min(d, sqrt((x; – x;)² + (y¡ – y;)²)) llsqrt is square root
return d
O(n(log n)²)
O(n²)
O (n)
O(n log n)

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