What algorithm might find a missing integer in O(n) time, from an array?
Say we have an array A with elements in a value-range {1,2,3...2n}. Half the elements are missing so length of A = n.
E.g:
A = [1,2,5,3,10] , n=5
Output = 4
What algorithm might find a missing integer in O(n) time, from an array?
Say we have an array A with elements in a value-range {1,2,3...2n}. Half the elements are missing so length of A = n.
E.g:
A = [1,2,5,3,10] , n=5
Output = 4
The smallest missing integer must be in the range [1, ..., n+1]. So create an array of flags, all initially false, indicating the presence of that integer. Then an algorithm is:
flag[A[i]] to true for each position i in the input array, provided A[i] <= n.)EDIT: O(n) time algorithm with O(1) extra space:
If A is writable and there are some extra bits available in the elements of A, then a constant-extra-space algorithm is possible. For instance, if the elements of A are signed values, and since all the numbers are positive, we can use the sign bit of the numbers in the original array as the flags, rather than creating a new flag array. So the algorithm would be:
i of the original array, if abs(A[i]) < n+1, make the value at A[abs(A[i])] negative. (This assumes array indexes are based at 1. Adjust in the obvious way if you are using 0-based arrays.) Don't just negate the value, in case there are duplicate values in A.A that is positive. That index is the smallest missing number in A. If all positions are negative, then A must be a permutation of {1, ..., n} and hence the smallest missing number is n+1.If the elements are unsigned, but can hold values as high as 4 n + 1, then in step 1, instead of making the element negative, add 2 n + 1 (provided the element is <= 2 n) and use (A[i] mod (2n+1)) instead of abs(A[i]). Then in step 2, find the first element < 2 n + 1 instead of the first positive element. Other such tricks are possible as well.
You can do this in O(1) additional space, assuming that the only valid operations on the array is to read elements, and to swap pairs of elements.
First note that the specification of the problem excludes the possibility of the array containing duplicates: it contains half of the numbers from 1 to 2N.
We perform a quick-select type algorithm. Start with m=1, M=2N+1, and pivot the array on (m + M)/2. If the size of the left part of the array (elements <= (m+M)/2) is less than (m + M)/2 - m + 1, then the first missing number must be there. Otherwise, it must be in the right part of the array. Repeat on the left or right side accordingly until you find the missing number.
The size of the slice of the array under consideration halves each time and pivoting an array of size n can be done in O(n) time and O(1) space. So overall, the time complexity is 2N + N + N/2 + ... + 1 <= 4N = O(N).
An implementation of Paul Hankin's idea in C++
#include <iostream>
using namespace std;
const int MAX = 1000;
int a[MAX];
int n;
void swap(int &a, int &b) {
int tmp = a;
a = b;
b = tmp;
}
// Rearranges elements of a[l..r] in such a way that first come elements
// lower or equal to M, next come elements greater than M. Elements in each group
// come in no particular order.
// Returns an index of the first element among a[l..r] which is greater than M.
int rearrange(int l, int r, int M) {
int i = l, j = r;
while (i <= j)
if (a[i] <= M) i++;
else swap(a[i], a[j--]);
return i;
}
int main() {
cin >> n;
for (int i = 0; i < n; i++) cin >> a[i];
int L = 1, R = 2 * n;
int l = 0, r = n - 1;
while (L < R) {
int M = (L + R) / 2; // pivot element
int m = rearrange(l, r, M);
if (m - l == M - L + 1)
l = m, L = M + 1;
else
r = m - 1, R = M;
}
cout << L;
return 0;
}