Finding the second Max in an unsorted dynamic array - c

I share with my solution of the algorithm to find the second maximum in a given unsorted array, is there a better way to do it? (I found complexity O(2n))
int PositionDuSecondMax(int *T, int n) {
// this function look for the index of the second maximum ( the number just under the maximum ) in a given array
// exemple : T[] = {10,100,14,49] ---> this function returns 3
// we have T a dynamic array and n the number of elements in T
// the given array in unsorted
int iMax = 0; // we suppose iMax == 0
int iSecMax;
// we go through the array and compare T[iMax] with the current element in the current index
// so we can find the index of the max in the array
for (int i = 0; i < n; i++) {
if (T[iMax] < T[i]) {
iMax = i;
}
}
// this if statement is to max sure that iMax is different from iSecMax
if (iMax == 0) {
iSecMax = 1;
} else {
iSecMax = iMax - 1;
}
// we loop through the array and compare each element with T[iSecMax] , we must specify that T[iSecMax] != T[iMax]
for (int i = 0; i < n; i++) {
if (T[iSecMax] < T[i] && T[iSecMax] == T[iMax]) {
iSecMax = i;
}
}
return iSecMax;
}

Your approach has multiple problems:
if the max is at offset 0 with a duplicate at offset 1 or if the max is at offset iMax - 1, the initial value of iSecMax is that of the maximum so you will not find the second max.
the test T[iSecMax] == T[iMax] to avoid select a duplicate of the max value is incorrect, it should be T[i] != T[iMax].
Here is a modified version:
int PositionDuSecondMax(const int *T, int n) {
// this function returns the index of the second maximum (the number just under the maximum) in a given array
// example: T[] = {10,100,14,49} --> this function returns 3
// arguments: T points to an unsorted array of int,
// n is the number of elements in T.
int iMax = 0;
int iSecMax = -1;
// we go through the array and compare T[iMax] with the
// element in the current index so we can find the index
// of the max in the array
for (int i = 1; i < n; i++) {
if (T[iMax] < T[i]) {
iMax = i;
}
}
// we loop through the array, ignoring occurrences of T[iMax] and
// compare each element with T[iSecMax]
for (int i = 0; i < n; i++) {
if (T[i] != T[iMax] && (iSecMax < 0 || T[iSecMax] < T[i])) {
iSecMax = i;
}
}
return iSecMax;
}
Notes:
the above function will return -1 if the array is empty or has all entries with the same value, ie: no second max value.
the complexity is O(n). O(2n) and O(n) are the same thing: O(n) means asymptotically proportional to n.
You could modify the code to perform a single scan with a more complicated test, and it would still be O(n).
If the array is unsorted, all elements must be tested so O(n) is the best one can achieve.
If the array was sorted, finding the second max would be O(1) on average (if the second to last element differs from the last), with a worst case of O(log n) (using binary search when the max has duplicates).

If you want to find second largest element in one traverse, you can follow below approach.
if (arrSize < 2)
{
printf(" Invalid Input ");
return;
}
first = second = INT_MIN;
for (i = 0; i < arrSize; i++)
{
/* If current element is greater than first
then update both first and second */
if (arr[i] > first)
{
second = first;
first = arr[i];
}
/* If arr[i] is in between first and
second then update second */
else if (arr[i] > second && arr[i] != first)
second = arr[i];
}
if (second == INT_MIN) printf("There is no second largest element\n");
else printf("The second largest element is %d\n", second);

The best approach is to visit each element of an array to find the second highest number in array with duplicates. The time complexity of this approach is O(n).
Algorithm :
i) Declare two variables max and second max and initialize them with integer minimum possible value.
ii) Traverse an array and compare each element of an array with the value assigned to max variable. If current element is greater than the value assigned at max variable. Then do two things –
a) In second max variable, assign the value present at max variable.
b) In max variable, assign the current index value.
iii) We have to do one more comparison that if current index value is less than max and greater than the value assigned at second max. Then, assign current index value at second max variable.
After complete iteration print the second max element of an array.
#include<stdio.h>
int secondLargest(int arr[], int len) {
//Initialize
int max = 0;
int second_max = 0;
for(int i = 0; i < len; i++) {
if(arr[i] > max) {
second_max = max;
max = arr[i];
}
if(max > arr[i] && arr[i] > second_max) {
second_max=arr[i];
}
}
return second_max;
}
int main(void) {
int arr[] = {70, 4, 8, 10, 14, 9, 7, 6, 5, 3, 2};
int len = 11;
printf("Second highest element is %d\n",secondLargest(arr,len));
return 0;
}

Related

How to find the number of elements in the array that are bigger than all elements after it?

I have a function that takes a one-dimensional array of N positive integers and returns the number of elements that are larger than all the next. The problem is exist a function to do it that in a better time? My code is the following:
int count(int *p, int n) {
int i, j;
int countNo = 0;
int flag = 0;
for(i = 0; i < n; i++) {
flag = 1;
for(j = i + 1; j < n; j++) {
if(p[i] <= p[j]) {
flag = 0;
break;
}
}
if(flag) {
countNo++;
}
}
return countNo;
}
My solution is O(n^2). Can it be done better?
You can solve this problem in linear time(O(n) time). Note that the last number in the array will always be a valid number that fits the problem definition. So the function will always output a value that will be greater than equal to 1.
For any other number in the array to be a valid number it must be greater than or equal to the greatest number that is after that number in the array.
So iterate over the array from right to left keeping track of the greatest number found till now and increment the counter if current number is greater than or equal to the greatest found till now.
Working code
int count2(int *p, int n) {
int max = -1000; //this variable represents negative infinity.
int cnt = 0;
int i;
for(i = n-1; i >=0; i--) {
if(p[i] >= max){
cnt++;
}
if(p[i] > max){
max = p[i];
}
}
return cnt;
}
Time complexity : O(n)
Space complexity : O(1)
It can be done in O(n).
int count(int *p, int n) {
int i, currentMax;
int countNo = 0;
currentMax = p[n-1];
for(i = n-1; i >= 0; i--) {
if(currentMax < p[i])
{
countNo ++;
currentMax = p[i];
}
}
return countNo;
}
Create an auxillary array aux:
aux[i] = max{arr[i+1], ... ,arr[n-1] }
It can be done in linear time by scanning the array from right to left.
Now, you only need the number of elements such that arr[i] > aux[i]
This is done in O(n).
Walk backwards trough the array, and keep track of the current maximum. Whenever you find a new maximum, that element is larger than the elements following.
Yes, it can be done in O(N) time. I'll give you an approach on how to go about it. If I understand your question correctly, you want the number of elements that are larger than all the elements that come next in the array provided the order is maintained.
So:
Let len = length of array x
{...,x[i],x[i+1]...x[len-1]}
We want the count of all elements x[i] such that x[i]> x[i+1]
and so on till x[len-1]
Start traversing the array from the end i.e. at i = len -1 and keep track of the largest element that you've encountered.
It could be something like this:
max = x[len-1] //A sentinel max
//Start a loop from i = len-1 to i = 0;
if(x[i] > max)
max = x[i] //Update max as you encounter elements
//Now consider a situation when we are in the middle of the array at some i = j
{...,x[j],....x[len-1]}
//Right now we have a value of max which is the largest of elements from i=j+1 to len-1
So when you encounter an x[j] that is larger than max, you've essentially found an element that's larger than all the elements next. You could just have a counter and increment it when that happens.
Pseudocode to show the flow of algorithm:
counter = 0
i = length of array x - 1
max = x[i]
i = i-1
while(i>=0){
if(x[i] > max){
max = x[i] //update max
counter++ //update counter
}
i--
}
So ultimately counter will have the number of elements you require.
Hope I was able to explain you how to go about this. Coding this should be a fun exercise as a starting point.

Counting occurrences of the largest number

I have been attempting to code for a program that stores input into an array and then allows me to print it out. It also lets me know which number is the largest. What I am trying to figure out is how can I get my program to tell me the amount of times (occurrences) the largest number in array is input. Here is my code so far. As of now, this code outputs the numbers I enter to the array, the largest element in the array, and the occurrence of every number I input( The occurrences of the numbers are incorrect). In all the the amount of occurrences for every number turns out to be 0. Which is obviously incorrect. Again, I need my program to display the largest number (which it does) and the occurrences of ONLY the largest number. All advice, tips, or thoughts are welcome. Thank you.
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <ctype.h>
int main()
{
int arrayNum[15];
int a;
int max=0;
int location;
for( a=0; a < 15; a++)
{
printf("Enter element %d:", a);
scanf("%d",&arrayNum[a]);
}
for(a=0; a < 15; a++)
{
printf("%d\n", arrayNum[a]);
}
for (a = 1; a < 15; a++)
{
if (arrayNum[a] > max)
{
max = arrayNum[a];
location = a+1;
}
}
printf("Max element in the array in the location %d and its value %d\n", location, max);
for(a=0; a<15; a++)
{
if(arrayNum[a+1] == arrayNum[a])
continue;
else
printf("Number %d: %d occurences\n", arrayNum[a]);
}
return 0;
}
I spot some problems in your code. First, the third for loop starts at 1, but it does not update the max as the value of arrayNum[0].
Then, for the problem at hand, I would have two variables:
int max; // The maximum value
int max_count; // The count of the maximum value
Then, the logic to find the greatest, and the count, is the following:
For each element, compare it with the maximum seen. If it is equal, increment max_count. If it is bigger, update max with the value, and set the max_count to 1. If it is smaller, ignore it. Something like:
max = arrayNum[0];
max_count = 1;
for (int a = 1; a < 15; ++a)
{
if (arrayNum[a] == max)
max_count++;
else if (arrayNum[a] > max)
{
max_count = 1;
max = arrayNum[a];
}
}
All you need to do is introduce a new variable to keep track of the number of occurrences of max. When a new value of max is found, set that count to zero. When a subsequent value is found equal to the max, increment the counter.
Incidentally, your code doesn't properly find the maximum in its current form. Try one test case where your array elements are all negative. Try another test case in which all the values are positive, and the first value entered (arrayNum[0]) is the maximum. You will find, in both cases, that your function will not actually find the maximum.
Just before you begin the below loop max is still 0 make
max = a[0];
for (a = 1; a < 15; a++)
{
if (arrayNum[a] > max)
{
max = arrayNum[a];
location = a+1;
}
}
Later
int n=0;
for(i=0;i<15;i++)
{
if(max == a[i])
n++;
}
printf("Number of times max appears in the array is %d\n",n);
Replace last for loop with below code
NoOfOccurances = 0;
for(a=0; a<15; a++)
{
if(max == arrayNum[a])
{
NoOfOccurances++;
}
}
printf("Number %d: %d occurences\n", max,NoOfOccurances);
For your third for-loop, the one where you find out the largest number in your array, I would suggest to set max to arrayNum[0], that way it will work even with negative numbers.
Then, to know how many occurrence of the highest number there is, you need a count variable that you increment (count++) each time a number of the array is equal to max. To do that you need another for-loop.
Good luck.
You can do what you want in just one loop iteration:
int count = 1;
int position = 0;
int max = arrayNum[0];
int N = 15;
int p;
for (p = 1; p < N; ++p)
{
if (arrayNum[p] > max) // Find a bigger number
{
max = arrayNum[p];
pos = p;
count = 1;
}
else if ( arrayNum[p] == max) // Another occurrences of the same number
count++;
}
A simple solution with time complexity of O(n)
int maxoccurence(int a[],int ar_size)
{
int max=a[0],count=0,i;
for(i=0;i<ar_size;i++)
{
if(a[i]==max)//counting the occurrence of maximum element
count++;
if(a[i]>max)//finding maximum number
{
max=a[i];
count=1;
}
}
printf("Maximum element in the array is %d\n",max);
return count;
}

issue with a sort method (C)

I'm having issues with a sort method I wrote. It is supposed to find the max value, and replace the last value in the array with the max (and move that value to where the last value was).
I've ran gdb, and it looks like the if statement always executes, and for some reason max = values[0] always sets max to 0. Granted I am very new to C so I might be wrong about what is going on.
/**
* Sorts array of n values.
*/
void sort(int values[], int n)
{
// TODO: implement an O(n^2) sorting algorithm
int max; //hold the max value through the iteration
int replaced; //to hold the value at the end of the array
int replacedhash; //to hold the location of the max value
do
{
replaced = values[n];
max = values[0]; //reset max to 0 for new iteration
for(int i = 0; i<n ; i++)
{
//check if the next value is larger,
//then update max and replacedhash if it is
if (max < values[i])
{
max = values[i];
replacedhash = i;
}
}
values[replacedhash] = replaced; //next three lines swap the values
n--;
values[n] = max;
} while (n!=0);
}
And I would use this by running:
int main() {
int test[] = {3,5,2,5,6,100,4,46};
sort(test, 8);
printarray(test, 8);
}
Error 1: replaced = values[n-1];
Your example in the problem statement is:
int test[] = {3,5,2,5,6,100,4,46};
sort(test, 8);
So you'll then look at test[8], which is undefined behavior
Error 2: replacedhash
replacedhash will be uninitialized if the first element of the array is the max. And it will probably have an incorrect value on later loops when the first element is the max.
My thoughts:
It appears to me that you've overcomplicating the code. You probably should just find the index in the array that has the maximum value, and then do the swap. It'll be simpler.
void sort(int values[], int n) {
do {
// Find index of maximum value
int max = 0;
for(int i=0; i<n; i++)
if (values[max] < values[i])
max = i;
// Swap
int temp = values[max];
values[max] = values[n-1];
values[n-1] = temp;
n--;
} while (n != 0);
}

Rearrange an array so that arr[i] becomes arr[arr[i]] with O(1) extra space

The task is to rearrange an array so that arr[i] becomes arr[arr[i]] with O(1) extra space.
Example:
2 1 3 5 4 0
becomes:
3 1 5 0 4 2
I can think of an O(n²) solution. An O(n) solution was presented here:
Increase every array element arr[i] by (arr[arr[i]] % n)*n.
Divide every element by n.
But this is very limited as it will cause buffer overflow.
Can anyone come up with an improvement upon this?
If the values in the array are all positive (or all negative), one way to avoid overflow could be to run the permutation cycles and use the integer sign to mark visited indexes. (Alternatively, if the array length is smaller than 2^(number of bits for one array element - 1), rather than use the sign, we could shift all the values one bit to the left and use the first bit to mark visited indexes.) This algorithm results in both less iterations and less modifications of the original array values during run-time than the algorithm you are asking to improve.
JSFiddle: http://jsfiddle.net/alhambra1/ar6X6/
JavaScript code:
function rearrange(arr){
var visited = 0,tmp,indexes,zeroTo
function cycle(startIx){
tmp = {start: startIx, value: arr[startIx]}
indexes = {from: arr[startIx], to: startIx}
while (indexes.from != tmp.start){
if (arr[indexes.from] == 0)
zeroTo = indexes.to
if (indexes.to == visited){
visited++
arr[indexes.to] = arr[indexes.from]
} else {
arr[indexes.to] = -arr[indexes.from]
}
indexes.to = indexes.from
if (indexes.from != tmp.start)
indexes.from = arr[indexes.from]
}
if (indexes.to == visited){
visited++
arr[indexes.to] = tmp.value
} else {
arr[indexes.to] = -tmp.value
}
}
while (visited < arr.length - 1){
cycle(visited)
while (arr[visited] < 0 || visited == zeroTo){
arr[visited] = -arr[visited]
visited++
}
}
return arr
}
//Traverse the array till the end.
//For every index increment the element by array[array[index] % n]. To get //the ith element find the modulo with n, i.e array[index]%n.
//Again traverse to end
//Print the ith element after dividing the ith element by n, i.e. array[i]/n
class Rearrange
{
void rearrange(int arr[], int n)
{
for (int i = 0; i < n; i++)
arr[i] += (arr[arr[i]] % n) * n;
for (int i = 0; i < n; i++)
arr[i] /= n;
}
void printArr(int arr[], int n)
{
for (int i = 0; i < n; i++)
System.out.print(arr[i] + " ");
System.out.println("");
}
public static void main(String[] args)
{
Rearrange rearrange = new Rearrange();
int arr[] = {6, 4, 9, 2, 5, 7};
int n = arr.length;
System.out.println("Given Array is :");
rearrange.printArr(arr, n);
rearrange.rearrange(arr, n);
System.out.println("Modified Array is :");
rearrange.printArr(arr, n);
}
}

Find the number of remove-then-append operations needed to sort a given array

This is an interview question. A swap means removing any element from the array and appending it to the back of the same array. Given an array of integers, find the minimum number of swaps needed to sort the array.
Is there a solution better than O(n^2)?
For example:
Input array: [3124].
The number of swaps: 2 ([3124] -> [1243] -> [1234]).
The problem boils down to finding the longest prefix of the sorted array that appears as a subsequence in the input array. This determines the elements that do not need to be sorted. The remaining elements will need to be deleted one by one, from the smallest to the largest, and appended at the back.
In your example, [3, 1, 2, 4], the already-sorted subsequence is [1, 2]. The optimal solution is to delete the remaning two elements, 3 and 4, and append them at the back. Thus the optimal solution is two "swaps".
Finding the subsequence can be done in O(n logn) time using O(n) extra memory. The following pseudo-code will do it (the code also happens to be valid Python):
l = [1, 2, 4, 3, 99, 98, 7]
s = sorted(l)
si = 0
for item in l:
if item == s[si]:
si += 1
print len(l) - si
If, as in your example, the array contains a permutation of integers from 1 to n, the problem can be solved in O(n) time using O(1) memory:
l = [1, 2, 3, 5, 4, 6]
s = 1
for item in l:
if item == s:
s += 1
print len(l) - s + 1
More generally, the second method can be used whenever we know the output array a priori and thus don't need to find it through sorting.
This might work in O(nlogn) even if we don't assume array of consecutive values.
If we do - it can be done in O(n).
One way of doing it is with O(n) space and O(nlogn) time.
Given array A sort it (O(nlogn)) into a second array B.
now... (arrays are indexed from 1)
swaps = 0
b = 1
for a = 1 to len(A)
if A[a] == B[b]
b = b + 1
else
swaps = swaps + 1
Observation: If an element is swapped to the back, its previous position does not matter. No element needs to be swapped more than once.
Observation: The last swap (if any) must move the largest element.
Observation: Before the swap, the array (excluding the last element) must be sorted (by former swaps, or initially)
Sorting algorithm, assuming the values are conecutive: find the longest sorted subsequence of consecutive (by value) elements starting at 1:
3 1 5 2 4
swap all higher elements in turn:
1 5 2 4 3
1 5 2 3 4
1 2 3 4 5
To find the number of swaps in O(n), find the length of the longest sorted subsequence of consecutive elements starting at 1:
expected = 1
for each element in sequence
if element == expected
expected += 1
return expected-1
then the number of swaps = the length of the input - its longest sorted subsequence.
An alternative solution ( O(n^2) ) if the input is not a permutation of 1..n:
swaps = 0
loop
find the first instance of the largest element and detect if the array is sorted
if the array is sorted, return swaps.
else remove the found element from the array and increment swaps.
Yet another solution ( O(n log n) ), assuming unique elements:
wrap each element in {oldPos, newPos, value}
make a shallow copy of the array
sort the array by value
store the new position of each element
run the algorithm for permutations on the newPos' in the (unsorted) copy
If you don't want to copy the input array, sort by oldPos before the last step instead.
This can be done in O(n log n).
First find the minimum element in the array. Now, find the max element that occurs before this element. Call this max_left. You have to call swap()for all the elements before the min element of the array.
Now, find the longest increasing subsequence to the right of the min element, along with the constraint that you should skip elements whose values are greater than max_left.
The required number of swaps is size(array) - size(LIS).
For example consider the array,
7 8 9 1 2 5 11 18
Minimum element in the array is 1. So we find the max before the minimum element.
7 8 9 | 1 2 5 11 18
max_left = 9
Now, find the LIS to the right of min with elements < 9
LIS = 1,2,5
No of swaps = 8 - 3 = 5
In cases where max element is null, ie., min is the first element, find the LIS of the array and required answer is size(array)-size(LIS)
For Example
2 5 4 3
max_left is null. LIS is 2 3
No of swaps = size(array) - size(LIS) = 4 - 2 = 2
Here is the code in python for minimum number of swaps,
def find_cycles(array):
cycles = []
remaining = set(array)
while remaining:
j = i = remaining.pop()
cycle = [i]
while True:
j = array[j]
if j == i:
break
array.append(j)
remaining.remove(j)
cycles.append(cycle)
return cycles
def minimum_swaps(seq):
return sum(len(cycle) - 1 for cycle in find_cycles(seq))
O(1) space and O(N) (~ 2*N) solution assuming min element is 1 and the array contains all numbers from 1 to N-1 without any duplicate value. where N is array length.
int minimumSwaps(int[] a) {
int swaps = 0;
int i = 0;
while(i < a.length) {
int position = a[i] - 1;
if(position != i) {
int temp = a[position];
a[position] = a[i];
a[i] = temp;
swaps++;
} else {
i++;
}
}
return swaps;
}
int numSwaps(int arr[], int length) {
bool sorted = false;
int swaps = 0;
while(!sorted) {
int inversions = 0;
int t1pos,t2pos,t3pos,t4pos = 0;
for (int i = 1;i < length; ++i)
{
if(arr[i] < arr[i-1]){
if(inversions){
tie(t3pos,t4pos) = make_tuple(i-1, i);
}
else tie(t1pos, t2pos) = make_tuple(i-1, i);
inversions++;
}
if(inversions == 2)
break;
}
if(!inversions){
sorted = true;
}
else if(inversions == 1) {
swaps++;
int temp = arr[t2pos];
arr[t2pos] = arr[t1pos];
arr[t1pos] = temp;
}
else{
swaps++;
if(arr[t4pos] < arr[t2pos]){
int temp = arr[t1pos];
arr[t1pos] = arr[t4pos];
arr[t4pos] = temp;
}
else{
int temp = arr[t2pos];
arr[t2pos] = arr[t1pos];
arr[t1pos] = temp;
}
}
}
return swaps;
}
This code returns the minimal number of swaps required to sort an array inplace.
For example, A[] = [7,3,4,1] By swapping 1 and 7, we get [1,3,4,7].
similarly B[] = [1,2,6,4,8,7,9]. We first swap 6 with 4, so, B[] -> [1,2,4,6,8,7,9]. Then 7 with 8. So -> [1,2,4,6,7,8,9]
The algorithm runs in O(number of pairs where value at index i < value at index i-1) ~ O(N) .
Writing a very simple JavaScript program to sort an array and find number of swaps:
function findSwaps(){
let arr = [4, 3, 1, 2];
let swap = 0
var n = arr.length
for (let i = 0; i < n; i++) {
for (let j = i + 1; j < n; j++) {
if (arr[i] > arr[j]) {
arr[i] = arr[i] + arr[j];
arr[j] = arr[i] - arr[j];
arr[i] = arr[i] - arr[j]
swap = swap + 1
}
}
}
console.log(arr);
console.log(swap)
}
for(int count = 1; count<=length; count++)
{
tempSwap=0; //it will count swaps per iteration
for(int i=0; i<length-1; i++)
if(a[i]>a[i+1])
{
swap(a[i],a[i+1]);
tempSwap++;
}
if(tempSwap!=0) //check if array is already sorted!
swap += tempSwap;
else
break;
}
System.out.println(swaps);
this is an O(n) solution which works for all inputs:
static int minimumSwaps(int[] arr) {
int swap=0;
boolean visited[]=new boolean[arr.length];
for(int i=0;i<arr.length;i++){
int j=i,cycle=0;
while(!visited[j]){
visited[j]=true;
j=arr[j]-1;
cycle++;
}
if(cycle!=0)
swap+=cycle-1;
}
return swap;
}
}
def minimumSwaps(arr):
swaps = 0
'''
first sort the given array to determine the correct indexes
of its elements
'''
temp = sorted(arr)
# compare unsorted array with the sorted one
for i in range(len(arr)):
'''
if ith element in the given array is not at the correct index
then swap it with the correct index, since we know the correct
index because of sorting.
'''
if arr[i] != temp[i]:
swaps += 1
a = arr[i]
arr[arr.index(temp[i])] = a
arr[i] = temp[i]
return swaps
I think this problem can be solved in O(N) if you notice that an element in the array needs to be removed and appended if:
There is a smaller element to the right or...
There is a smaller element to his left that needs to be removed and appended.
Then it's just about identifying elements that will need to be removed and appended. Here is the code:
static int minMoves(int arr[], int n) {
if (arr.length == 0) return 0;
boolean[] willBeMoved = new boolean[n]; // keep track of elements to be removed and appended
int min = arr[n - 1]; // keep track of the minimum
for (int i = n - 1; i >= 0; i--) { // traverse the array from the right
if (arr[i] < min) min = arr[i]; // found a new min
else if (arr[i] > min) { // arr[i] has a smaller element to the right, so it will need to be moved at some point
willBeMoved[i] = true;
}
}
int minToBeMoved = -1; // keep track of the minimum element to be removed and appended
int result = 0; // the answer
for (int i = 0; i < n; i++) { // traverse the array from the left
if (minToBeMoved == -1 && !willBeMoved[i]) continue; // find the first element to be moved
if (minToBeMoved == -1) minToBeMoved = i;
if (arr[i] > arr[minToBeMoved]) { // because a smaller value will be moved to the end, arr[i] will also have to be moved at some point
willBeMoved[i] = true;
} else if (arr[i] < arr[minToBeMoved] && willBeMoved[i]) { // keep track of the min value to be moved
minToBeMoved = i;
}
if (willBeMoved[i]) result++; // increment
}
return result;
}
It uses O(N) space.
#all , the accepted solution provided by #Itay karo and #NPE is totally wrong because it doesn't consider future ordering of swapped elements...
It fails for many testcases like:
3 1 2 5 4
correct output: 4
but their codes give output as 3...
explanation: 3 1 2 5 4--->1 2 5 4 3--->1 2 4 3 5--->1 2 3 5 4--->1 2 3 4 5
PS:i cann't comment there because of low reputation
Hear is my solution in c# to solve the minimum number of swaps required to short an array
At at time we can swap only 2 elements(at any index position).
public class MinimumSwaps2
{
public static void minimumSwapsMain(int[] arr)
{
Dictionary<int, int> dic = new Dictionary<int, int>();
Dictionary<int, int> reverseDIc = new Dictionary<int, int>();
int temp = 0;
int indx = 0;
//find the maximum number from the array
int maxno = FindMaxNo(arr);
if (maxno == arr.Length)
{
for (int i = 1; i <= arr.Length; i++)
{
dic[i] = arr[indx];
reverseDIc.Add(arr[indx], i);
indx++;
}
}
else
{
for (int i = 1; i <= arr.Length; i++)
{
if (arr.Contains(i))
{
dic[i] = arr[indx];
reverseDIc.Add(arr[indx], i);
indx++;
}
}
}
int counter = FindMinSwaps(dic, reverseDIc, maxno);
}
static int FindMaxNo(int[] arr)
{
int maxNO = 0;
for (int i = 0; i < arr.Length; i++)
{
if (maxNO < arr[i])
{
maxNO = arr[i];
}
}
return maxNO;
}
static int FindMinSwaps(Dictionary<int, int> dic, Dictionary<int, int> reverseDIc, int maxno)
{
int counter = 0;
int temp = 0;
for (int i = 1; i <= maxno; i++)
{
if (dic.ContainsKey(i))
{
if (dic[i] != i)
{
counter++;
var myKey1 = reverseDIc[i];
temp = dic[i];
dic[i] = dic[myKey1];
dic[myKey1] = temp;
reverseDIc[temp] = reverseDIc[i];
reverseDIc[i] = i;
}
}
}
return counter;
}
}
int temp = 0, swaps = 0;
for (int i = 0; i < arr.length;) {
if (arr[i] != i + 1){
// System.out.println("Swapping --"+arr[arr[i] - 1] +" AND -- "+arr[i]);
temp = arr[arr[i] - 1];
arr[arr[i] - 1] = arr[i];
arr[i] = temp;
++swaps;
} else
++i;
// System.out.println("value at position -- "+ i +" is set to -- "+ arr[i]);
}
return swaps;
This is the most optimized answer i have found. It is so simple. You will probably understand in one look through the loop. Thanks to Darryl at hacker rank.

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