Consider the following code fragment: static int f(int[ ] arr, int x ) { int start = 0; int end - arr.length -1; int mid; while(start <- end) { mid = start + ((end - start)/2); if(arr[ mid ] < x) { start - mid+1; } else if(arr[ mid ] > x) { end = mid-1; } else { return mid; } } return -1; } Give the worst case big-O cost where the length of the array is N. O O(N^2) O 0(1) O O(2^N) O O(logN) O O(N)
Consider the following code fragment: static int f(int[ ] arr, int x ) { int start = 0; int end - arr.length -1; int mid; while(start <- end) { mid = start + ((end - start)/2); if(arr[ mid ] < x) { start - mid+1; } else if(arr[ mid ] > x) { end = mid-1; } else { return mid; } } return -1; } Give the worst case big-O cost where the length of the array is N. O O(N^2) O 0(1) O O(2^N) O O(logN) O O(N)
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Question
![Consider the following code fragment:
static int f(int[ ] arr, int x ) {
int start = 0;
int end - arr.length -13;
int mid;
while(start <= end) {
mid = start + ((end - start)/2);
if(arr[ mid ] < x) {
start
mid+1;
} else if(arr[ mid ] > x) {
end - mid-1;
} else {
return mid;
}
}
return -1;
}
Give the worst case big-O cost where the length of the array is N.
O O(N^2)
O (1)
O 0(2^N)
O O(logN)
O(N)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F8bfe1080-e0d5-4baa-b4e3-fde34e28e788%2F1e57149c-626e-4fea-aa1d-1bdba946ecd6%2F2y03qpp_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Consider the following code fragment:
static int f(int[ ] arr, int x ) {
int start = 0;
int end - arr.length -13;
int mid;
while(start <= end) {
mid = start + ((end - start)/2);
if(arr[ mid ] < x) {
start
mid+1;
} else if(arr[ mid ] > x) {
end - mid-1;
} else {
return mid;
}
}
return -1;
}
Give the worst case big-O cost where the length of the array is N.
O O(N^2)
O (1)
O 0(2^N)
O O(logN)
O(N)
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