I'm having some problems solving this question:
Given an array of ints, divide the input into 2 groups such that their sums are as close as possible, the 2 groups must be equal in length, or if the input is odd length then one group can have 1 more than the other. Then print the lower sum first, and the higher sum after.
Ex:
input -> [4,6,17,3,2,5,10]
output -> 23,24 ([17,5,2] , [10,6,4,3])
This is what I've come up with and so far what I've tested it's passed but I do not know if it's actually correct:
public static String closestSums(int[] input) {
Integer sum1 = 0;
Integer sum2 = 0;
Integer dif = 0;
Integer bigSum = 0;
List<Integer> list = new ArrayList<>();
List<Integer> list1 = new ArrayList<>();
List<Integer> list2 = new ArrayList<>();
for (int x = 0; x < input.length; x++) {
list.add(input[x]);
}
Collections.sort(list);
for (int x = list.size(); x >= 0; x--) {
bigSum += list.get(x);
if (dif == 0) {
dif = list.get(x);
list2.add(list.get(x));
}
else if (dif > 0) {
dif -= list.get(x);
list1.add(list.get(x));
}
else {
dif += list.get(x);
list2.add(list.get(x));
}
}
dif = Math.abs(dif);
if (dif != 0) {
sum2 = (bigSum / 2) + dif;
sum1 = bigSum / 2;
}
else {
sum2 = bigSum / 2;
sum1 = bigSum / 2;
}
return sum1 + ", " + sum2;
}
It's not correct.
Regardless of the bunch of small coding mistakes that are mentioned in the other answer, your algorithm doesn't work.
Consider the input [3, 3, 2, 2, 2]. Your algorithm will divide it into [3, 2, 2] and [3, 2] with a difference of 7-5=2. However, a better split with equal sums exists: [3, 3] and [2, 2, 2].
I am not going to provide a complete algorithm but give you a few hints:
1 - The minimum difference can be binary-searched. If you can come up with an algorithm that decides whether it is possible to split the array so that the difference of the sums of the pieces is at most d for a given d, then you can binary search the minimum d for which the algorithm outputs 1.
2 - Look at the subset sum problem, it can help with the subproblem I defined in item 1.
I have made 3 observations after analyzing & running your Code.
There lot of small errors throughout the code.
Syntaxical Error On Line 4 (Initialization of 'Dif') Line 11(The 'for' keyword)
Logical Error on Line 26, Line 27 (Instead of Accessing Index 'i', you must access index 'x')
Runtime Error on Line 17 (Initializing x=list.size() will throw a runtime error java.lang.IndexOutOfBoundsException)
Though, the important things to look at, are mentioned below
It's a suggestion, if you aren't printing the list with the 'sum1' & 'sum2', then creating and operating the two other output lists is redundant. The respective Sums of the list can be calculated while traversing the main list.
Most importantly, the way you are calculating sum1 and sum2 after traversing the list and then dividing them, is not reliable.
Eg. The Following input will give an unexpected output [4,6,45,3,2,5,10]. So, I suggest you to resort to the most reliable way of calculating the sum of list 1 & list 2, which is calculating them while traversing the list. (You may also remove the full logic of bigSum)
Last Words: Try and Implement the above mentioned Ideas yourself.
The task is to find lost element in the array. I understand the logic of the solution but I don't understand how does this formula works?
Here is the solution
int[] array = new int[]{4,1,2,3,5,8,6};
int size = array.length;
int result = (size + 1) * (size + 2)/2;
for (int i : array){
result -= i;
}
But why we add 1 to total size and multiply it to total size + 2 /2 ?? In all resources, people just use that formula but nobody explains how that formula works
The sum of the digits 1 thru n is equal to ((n)(n+1))/2.
e.g. for 1,2,3,4,5 5*6/2 = 15.
But this is just a quick way to add up the numbers from 1 to n. Here is what is really going on.
The series computes the sum of 1 to n assuming they all were present. But by subtracting each number from that sum, the remainder is the missing number.
The formula for an arithmetic series of integers from k to n where adjacent elements differ by 1 is.
S[k,n] = (n-k+1)(n+k)/2
Example: k = 5, n = 10
S[k,n] = 5 6 7 8 9 10
S[k,n] = 10 9 8 7 6 5
S[k,n] = (10-5+1)*(10+5)/2
2S[k,n] = 6 * 15 / 2
S[k,n] = 90 / 2 = 45
For any single number missing from the sequence, by subtracting the others from the sum of 45, the remainder will be the missing number.
Let's say you currently have n elements in your array. You know that one element is missing, which means that the actual size of your array should be n + 1.
Now, you just need to calculate the sum 1 + 2 + ... + n + (n+1).
A handy formula for computing the sum of all integers from 1 up to k is given by k(k+1)/2.
By just replacing k with n+1, you get the formula (n+1)(n+2)/2.
It's simple mathematics.
Sum of first n natural numbers = n*(n+1)/2.
Number of elements in array = size of array.
So, in this case n = size + 1
So, after finding the sum, we are subtracting all the numbers from array individually and we are left with the missing number.
Broken sequence vs full sequence
But why we add 1 to total size and multiply it to total size + 2 /2 ?
The amount of numbers stored in your array is one less than the maximal number, as the sequence is missing one element.
Check your example:
4, 1, 2, 3, 5, 8, 6
The sequence is supposed to go from 1 to 8, but the amount of elements (size) is 7, not 8. Because the 7 is missing from the sequence.
Another example:
1, 2, 3, 5, 6, 7
This sequence is missing the 4. The full sequence would have a length of 7 but the above array would have a length of 6 only, one less.
You have to account for that and counter it.
Sum formula
Knowing that, the sum of all natural numbers from 1 up to n, so 1 + 2 + 3 + ... + n can also be directly computed by
n * (n + 1) / 2
See the very first paragraph in Wikipedia#Summation.
But n is supposed to be 8 (length of the full sequence) in your example, not 7 (broken sequence). So you have to add 1 to all the n in the formula, receiving
(n + 1) * (n + 2) / 2
I guess this would be similar to Missing Number of LeetCode (268):
Java
class Solution {
public static int missingNumber(int[] nums) {
int missing = nums.length;
for (int index = 0; index < nums.length; index++)
missing += index - nums[index];
return missing;
}
}
C++ using Bit Manipulation
class Solution {
public:
int missingNumber(vector<int> &nums) {
int missing = nums.size();
int index = 0;
for (int num : nums) {
missing = missing ^ num ^ index;
index++;
}
return missing;
}
};
Python I
class Solution:
def missingNumber(self, nums):
return (len(nums) * (-~len(nums))) // 2 - sum(nums)
Python II
class Solution:
def missingNumber(self, nums):
return (len(nums) * ((-~len(nums))) >> 1) - sum(nums)
Reference to how it works:
The methods have been explained in the following links:
Missing Number Discussion
Missing Number Solution
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I am trying to find the O(N) solution with Divide & Conquer for the next problem:
Given a circularly sorted array, I need the sum of the positive numbers on it. i.e:
If the array is: {-2, 0, 3, 4, 11, 13, -23, -15, -8}
Then the algorithm should return 31.
I think I'm close to it with the following code, but it's weirdly returning -17 and I cannot find the problem debugging:
public class Main {
private static final int[] TEST_VECTOR = new int[]{-2, 0, 3, 4, 11, 13, -23, -15, -8};
public static int sumPositives2(int[] vector) {
return maximumSum(vector,0, vector.length - 1);
}
// Function to find Maximum subarray sum using
// divide and conquer
public static int maximumSum(int[] A, int left, int right)
{
// If array contains only one element
if (right == left) {
return A[left];
}
// Find middle element of the array
int mid = (left + right) / 2;
// Find maximum subarray sum for the left subarray
// including the middle element
int leftMax = Integer.MIN_VALUE;
int sum = 0;
for (int i = mid; i >= left; i--)
{
if(A[i] > 0) {
sum += A[i];
}
}
// Find maximum subarray sum for the right subarray
// excluding the middle element
int rightMax = Integer.MIN_VALUE;
sum = 0; // reset sum to 0
for (int i = mid + 1; i <= right; i++)
{
if(A[i] > 0) {
sum += A[i];
}
}
// Recursively find the maximum subarray sum for left
// subarray and right subarray and tale maximum
int maxLeftRight = maximumSum(A, left, mid) +
maximumSum(A, mid + 1, right);
// return maximum of the three
return maxLeftRight + leftMax + rightMax;
}
public static void main(String[] args)
{
System.out.println("The Maximum sum of the subarray is " +
maximumSum(TEST_VECTOR, 0, TEST_VECTOR.length - 1));//Should be 31
}
}
Edit: Would this solution be O(N)?
Thank you for your time.
You can easily get an O(n) solution if you go through the array once and sum up only positive numbers. An enhanced for loop seems suitable:
public static void main(String[] args) {
int[] circularlySortedArray = {-2, 0, 3, 4, 11, 13, -23, -15, -8};
// define a variable for the sum
int sumOfPositives = 0;
// go through all numbers in the array (n numbers)
for (int number : circularlySortedArray) {
// check if the number is positive
if (number >= 0) {
// and add it to the sum variable
sumOfPositives += number;
}
}
// then print the result
System.out.println("The sum of positive numbers is " + sumOfPositives);
}
The output in this case is
The sum of positive numbers is 31
The sorting does not have any influence on the algorithm.
Your solution would be O(n) if it was working, but you don't really have to divide and conquer here because it cannot be done in less than O(n) anyway, it's not a binary search.
EDIT
Since the text and code above seems not to be satisfying enough, I re-thought my statement about divide and conquer here. The task may be done in less than n steps, but O(n) will still be correct. The sorting does provide a possibility of not going through all elements of the array, but that won't be achievable in every case.
Imagine the following arrays, which are all circularly sorted, and please have a look at the patterns below them, they may be the key to a divide-and-conquer solution here and/or to a solution with an average case (Big Theta) of less than n, while the worst case (Big O) will still stay O(n)…
The examples all have n = 7 elements and each one has p = 4 positive elements (including 0) and l = 3 negative elements. Iterating through all of them will always be O(n), which would be O(6) here. In some of the cases, the sorting provides information about what's at the end of the array, which enables a programmer to reduce the best case (Big Omega) to O(p + 1) = O(p) = O(4) in some of the situations:
The following array takes n steps because one has to check every element
{-3, -2, -1, 0, 1, 2, 3}
n n n p p p p
The next example takes n - 1 steps, because there is are two negative numbers after all the positives and you only have to find the first one in order to meet a break condition. That's because there was a negative number already at a lower index.
{-1, 0, 1, 2, 3, -2, -3}
n p p p p n n
This next one takes only p + 1 (which means O(p + 1) = O(p)) steps because you can break the loop at the first negative number found. Why? Because the array starts with the smallest possible number that is positive (by definition) and the first negative number found indicates no need for further processing.
{0, 1, 2, 3, -1, -2, -3}
p p p p n n n
This last example requires n steps again, because you are looking for positive numbers which are at the beginning and at the end of the array. There is no chance of directly knowing at which index they are.
{3, -3, -2, -1, 0, 1, 2}
p n n n p p p
The only way (I know) to optimize the average case would be implementing the break conditions of the loop according to the possible patterns. So store indexes and do checks based on them, but I think the effect won't be that huge.
This is my first approach, may be optimized in several ways, I have only tried it with the examples of this edit:
public static void main(String[] args) {
int[] circularlySortedArray = { 0, 1, 2, 3, -1, -2, -3 };
// define a variable for the sum of positive values
int sumOfPositives = 0;
// define a variable for the lowest index of a positive number
int firstPositiveIndex = -1;
// define a variable for the lowest positive number found
int smallesPositiveNumber = 0;
// start iterating the array
for (int i = 0; i < circularlySortedArray.length; i++) {
System.out.println("Current index: " + i
+ ", current value: " + circularlySortedArray[i]);
// provide a variable for the current number to make this code a little more
// readable
int number = circularlySortedArray[i];
// check if the current number is positive
if (number >= 0) {
// add it to the sum
sumOfPositives += number;
System.out.println("Added " + number
+ " to sumOfPositives (now: " + sumOfPositives + ")");
// check if it is the first positive number found
if (firstPositiveIndex < 0) {
// if yes, set the variable value accordingly
System.out.println("First and smallest positive number ("
+ number
+ ") found at index "
+ i);
firstPositiveIndex = i;
smallesPositiveNumber = number;
}
System.out.println("————————————————————————————————");
} else {
// break conditions based on index & value of the smallest positive number found
if (i > firstPositiveIndex && firstPositiveIndex > 0) {
System.out.println("Stopped the loop at index " + i);
break;
} else if (smallesPositiveNumber == 0 && firstPositiveIndex == 0) {
System.out.println("Stopped the loop at index " + i);
break;
}
System.out.println(number + " is not positive, skip it");
System.out.println("————————————————————————————————");
continue;
}
}
System.out.println("The sum of positive numbers is " + sumOfPositives);
}
What you're asking for is impossible.
The input array could potentially be all positive values which means you have to at least read and sum all elements. That's O(n).
Even if not all elements are positive, unless it's defined that no more than O(log n) elements are positive the same conclusion remains.
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I have an array of numbers from 1 to 100 (both inclusive). The size of the array is 100. The numbers are randomly added to the array, but there is one random empty slot in the array.
What is the quickest way to find that slot as well as the number that should be put in the slot? A Java solution is preferable.
You can do this in O(n). Iterate through the array and compute the sum of all numbers. Now, sum of natural numbers from 1 to N, can be expressed as Nx(N+1)/2. In your case N=100.
Subtract the sum of the array from Nx(N+1)/2, where N=100.
That is the missing number. The empty slot can be detected during the iteration in which the sum is computed.
// will be the sum of the numbers in the array.
int sum = 0;
int idx = -1;
for (int i = 0; i < arr.length; i++)
{
if (arr[i] == 0)
{
idx = i;
}
else
{
sum += arr[i];
}
}
// the total sum of numbers between 1 and arr.length.
int total = (arr.length + 1) * arr.length / 2;
System.out.println("missing number is: " + (total - sum) + " at index " + idx);
We can use XOR operation which is safer than summation because in programming languages if the given input is large it may overflow and may give wrong answer.
Before going to the solution, know that A xor A = 0. So if we XOR two identical numbers the value is 0.
Now, XORing [1..n] with the elements present in the array cancels the identical numbers. So at the end we will get the missing number.
// Assuming that the array contains 99 distinct integers between 1..99
// and empty slot value is zero
int XOR = 0;
for(int i=0; i<100; i++) {
if (ARRAY[i] != 0) // remove this condition keeping the body if no zero slot
XOR ^= ARRAY[i];
XOR ^= (i + 1);
}
return XOR;
//return XOR ^ ARRAY.length + 1; if your array doesn't have empty zero slot.
Let the given array be A with length N. Lets assume in the given array, the single empty slot is filled with 0.
We can find the solution for this problem using many methods including algorithm used in Counting sort. But, in terms of efficient time and space usage, we have two algorithms. One uses mainly summation, subtraction and multiplication. Another uses XOR. Mathematically both methods work fine. But programatically, we need to assess all the algorithms with main measures like
Limitations(like input values are large(A[1...N]) and/or number of
input values is large(N))
Number of condition checks involved
Number and type of mathematical operations involved
etc. This is because of the limitations in time and/or hardware(Hardware resource limitation) and/or software(Operating System limitation, Programming language limitation, etc), etc. Lets list and assess the pros and cons of each one of them.
Algorithm 1 :
In algorithm 1, we have 3 implementations.
Calculate the total sum of all the numbers(this includes the unknown missing number) by using the mathematical formula(1+2+3+...+N=(N(N+1))/2). Here, N=100. Calculate the total sum of all the given numbers. Subtract the second result from the first result will give the missing number.
Missing Number = (N(N+1))/2) - (A[1]+A[2]+...+A[100])
Calculate the total sum of all the numbers(this includes the unknown missing number) by using the mathematical formula(1+2+3+...+N=(N(N+1))/2). Here, N=100. From that result, subtract each given number gives the missing number.
Missing Number = (N(N+1))/2)-A[1]-A[2]-...-A[100]
(Note:Even though the second implementation's formula is derived from first, from the mathematical point of view both are same. But from programming point of view both are different because the first formula is more prone to bit overflow than the second one(if the given numbers are large enough). Even though addition is faster than subtraction, the second implementation reduces the chance of bit overflow caused by addition of large values(Its not completely eliminated, because there is still very small chance since (N+1) is there in the formula). But both are equally prone to bit overflow by multiplication. The limitation is both implementations give correct result only if N(N+1)<=MAXIMUM_NUMBER_VALUE. For the first implementation, the additional limitation is it give correct result only if Sum of all given numbers<=MAXIMUM_NUMBER_VALUE.)
Calculate the total sum of all the numbers(this includes the unknown missing number) and subtract each given number in the same loop in parallel. This eliminates the risk of bit overflow by multiplication but prone to bit overflow by addition and subtraction.
//ALGORITHM
missingNumber = 0;
foreach(index from 1 to N)
{
missingNumber = missingNumber + index;
//Since, the empty slot is filled with 0,
//this extra condition which is executed for N times is not required.
//But for the sake of understanding of algorithm purpose lets put it.
if (inputArray[index] != 0)
missingNumber = missingNumber - inputArray[index];
}
In a programming language(like C, C++, Java, etc), if the number of bits representing a integer data type is limited, then all the above implementations are prone to bit overflow because of summation, subtraction and multiplication, resulting in wrong result in case of large input values(A[1...N]) and/or large number of input values(N).
Algorithm 2 :
We can use the property of XOR to get solution for this problem without worrying about the problem of bit overflow. And also XOR is both safer and faster than summation. We know the property of XOR that XOR of two same numbers is equal to 0(A XOR A = 0). If we calculate the XOR of all the numbers from 1 to N(this includes the unknown missing number) and then with that result, XOR all the given numbers, the common numbers get canceled out(since A XOR A=0) and in the end we get the missing number. If we don't have bit overflow problem, we can use both summation and XOR based algorithms to get the solution. But, the algorithm which uses XOR is both safer and faster than the algorithm which uses summation, subtraction and multiplication. And we can avoid the additional worries caused by summation, subtraction and multiplication.
In all the implementations of algorithm 1, we can use XOR instead of addition and subtraction.
Lets assume, XOR(1...N) = XOR of all numbers from 1 to N
Implementation 1 => Missing Number = XOR(1...N) XOR (A[1] XOR A[2] XOR...XOR A[100])
Implementation 2 => Missing Number = XOR(1...N) XOR A[1] XOR A[2] XOR...XOR A[100]
Implementation 3 =>
//ALGORITHM
missingNumber = 0;
foreach(index from 1 to N)
{
missingNumber = missingNumber XOR index;
//Since, the empty slot is filled with 0,
//this extra condition which is executed for N times is not required.
//But for the sake of understanding of algorithm purpose lets put it.
if (inputArray[index] != 0)
missingNumber = missingNumber XOR inputArray[index];
}
All three implementations of algorithm 2 will work fine(from programatical point of view also). One optimization is, similar to
1+2+....+N = (N(N+1))/2
We have,
1 XOR 2 XOR .... XOR N = {N if REMAINDER(N/4)=0, 1 if REMAINDER(N/4)=1, N+1 if REMAINDER(N/4)=2, 0 if REMAINDER(N/4)=3}
We can prove this by mathematical induction. So, instead of calculating the value of XOR(1...N) by XOR all the numbers from 1 to N, we can use this formula to reduce the number of XOR operations.
Also, calculating XOR(1...N) using above formula has two implementations. Implementation wise, calculating
// Thanks to https://a3nm.net/blog/xor.html for this implementation
xor = (n>>1)&1 ^ (((n&1)>0)?1:n)
is faster than calculating
xor = (n % 4 == 0) ? n : (n % 4 == 1) ? 1 : (n % 4 == 2) ? n + 1 : 0;
So, the optimized Java code is,
long n = 100;
long a[] = new long[n];
//XOR of all numbers from 1 to n
// n%4 == 0 ---> n
// n%4 == 1 ---> 1
// n%4 == 2 ---> n + 1
// n%4 == 3 ---> 0
//Slower way of implementing the formula
// long xor = (n % 4 == 0) ? n : (n % 4 == 1) ? 1 : (n % 4 == 2) ? n + 1 : 0;
//Faster way of implementing the formula
// long xor = (n>>1)&1 ^ (((n&1)>0)?1:n);
long xor = (n>>1)&1 ^ (((n&1)>0)?1:n);
for (long i = 0; i < n; i++)
{
xor = xor ^ a[i];
}
//Missing number
System.out.println(xor);
This was an Amazon interview question and was originally answered here: We have numbers from 1 to 52 that are put into a 51 number array, what's the best way to find out which number is missing?
It was answered, as below:
1) Calculate the sum of all numbers stored in the array of size 51.
2) Subtract the sum from (52 * 53)/2 ---- Formula : n * (n + 1) / 2.
It was also blogged here: Software Job - Interview Question
Here is a simple program to find the missing numbers in an integer array
ArrayList<Integer> arr = new ArrayList<Integer>();
int a[] = { 1,3,4,5,6,7,10 };
int j = a[0];
for (int i=0;i<a.length;i++)
{
if (j==a[i])
{
j++;
continue;
}
else
{
arr.add(j);
i--;
j++;
}
}
System.out.println("missing numbers are ");
for(int r : arr)
{
System.out.println(" " + r);
}
Recently I had a similar (not exactly the same) question in a job interview and also I heard from a friend that was asked the exactly same question in an interview.
So here is an answer to the OP question and a few more variations that can be potentially asked.
The answers example are given in Java because, it's stated that:
A Java solution is preferable.
Variation 1:
Array of numbers from 1 to 100 (both inclusive) ... The numbers are randomly added to the array, but there is one random empty slot in the array
public static int findMissing1(int [] arr){
int sum = 0;
for(int n : arr){
sum += n;
}
return (100*(100+1)/2) - sum;
}
Explanation:
This solution (as many other solutions posted here) is based on the formula of Triangular number, which gives us the sum of all natural numbers from 1 to n (in this case n is 100). Now that we know the sum that should be from 1 to 100 - we just need to subtract the actual sum of existing numbers in given array.
Variation 2:
Array of numbers from 1 to n (meaning that the max number is unknown)
public static int findMissing2(int [] arr){
int sum = 0, max = 0;
for(int n : arr){
sum += n;
if(n > max) max = n;
}
return (max*(max+1)/2) - sum;
}
Explanation:
In this solution, since the max number isn't given - we need to find it. After finding the max number - the logic is the same.
Variation 3:
Array of numbers from 1 to n (max number is unknown), there is two random empty slots in the array
public static int [] findMissing3(int [] arr){
int sum = 0, max = 0, misSum;
int [] misNums = {};//empty by default
for(int n : arr){
sum += n;
if(n > max) max = n;
}
misSum = (max*(max+1)/2) - sum;//Sum of two missing numbers
for(int n = Math.min(misSum, max-1); n > 1; n--){
if(!contains(n, arr)){
misNums = new int[]{n, misSum-n};
break;
}
}
return misNums;
}
private static boolean contains(int num, int [] arr){
for(int n : arr){
if(n == num)return true;
}
return false;
}
Explanation:
In this solution, the max number isn't given (as in the previous), but it can also be missing of two numbers and not one. So at first we find the sum of missing numbers - with the same logic as before. Second finding the smaller number between missing sum and the last (possibly) missing number - to reduce unnecessary search. Third since Javas Array (not a Collection) doesn't have methods as indexOf or contains, I added a small reusable method for that logic. Fourth when first missing number is found, the second is the subtract from missing sum.
If only one number is missing, then the second number in array will be zero.
Variation 4:
Array of numbers from 1 to n (max number is unknown), with X missing (amount of missing numbers are unknown)
public static ArrayList<Integer> findMissing4(ArrayList<Integer> arr){
int max = 0;
ArrayList<Integer> misNums = new ArrayList();
int [] neededNums;
for(int n : arr){
if(n > max) max = n;
}
neededNums = new int[max];//zero for any needed num
for(int n : arr){//iterate again
neededNums[n == max ? 0 : n]++;//add one - used as index in second array (convert max to zero)
}
for(int i=neededNums.length-1; i>0; i--){
if(neededNums[i] < 1)misNums.add(i);//if value is zero, than index is a missing number
}
return misNums;
}
Explanation:
In this solution, as in the previous, the max number is unknown and there can be missing more than one number, but in this variation, we don't know how many numbers are potentially missing (if any). The beginning of the logic is the same - find the max number. Then I initialise another array with zeros, in this array index indicates the potentially missing number and zero indicates that the number is missing. So every existing number from original array is used as an index and its value is incremented by one (max converted to zero).
Note
If you want examples in other languages or another interesting variations of this question, you are welcome to check my Github repository for Interview questions & answers.
(sum of 1 to n) - (sum of all values in the array) = missing number
int sum = 0;
int idx = -1;
for (int i = 0; i < arr.length; i++) {
if (arr[i] == 0) idx = i; else sum += arr[i];
}
System.out.println("missing number is: " + (5050 - sum) + " at index " + idx);
On a similar scenario, where the array is already sorted, it does not include duplicates and only one number is missing, it is possible to find this missing number in log(n) time, using binary search.
public static int getMissingInt(int[] intArray, int left, int right) {
if (right == left + 1) return intArray[right] - 1;
int pivot = left + (right - left) / 2;
if (intArray[pivot] == intArray[left] + (intArray[right] - intArray[left]) / 2 - (right - left) % 2)
return getMissingInt(intArray, pivot, right);
else
return getMissingInt(intArray, left, pivot);
}
public static void main(String args[]) {
int[] array = new int[]{3, 4, 5, 6, 7, 8, 10};
int missingInt = getMissingInt(array, 0, array.length-1);
System.out.println(missingInt); //it prints 9
}
Well, use a bloom filter.
int findmissing(int arr[], int n)
{
long bloom=0;
int i;
for(i=0; i<;n; i++)bloom+=1>>arr[i];
for(i=1; i<=n, (bloom<<i & 1); i++);
return i;
}
This is c# but it should be pretty close to what you need:
int sumNumbers = 0;
int emptySlotIndex = -1;
for (int i = 0; i < arr.length; i++)
{
if (arr[i] == 0)
emptySlotIndex = i;
sumNumbers += arr[i];
}
int missingNumber = 5050 - sumNumbers;
The solution that doesn't involve repetitive additions or maybe the n(n+1)/2 formula doesn't get to you at an interview time for instance.
You have to use an array of 4 ints (32 bits) or 2 ints (64 bits). Initialize the last int with (-1 & ~(1 << 31)) >> 3. (the bits that are above 100 are set to 1) Or you may set the bits above 100 using a for loop.
Go through the array of numbers and set 1 for the bit position corresponding to the number (e.g. 71 would be set on the 3rd int on the 7th bit from left to right)
Go through the array of 4 ints (32 bit version) or 2 ints(64 bit version)
public int MissingNumber(int a[])
{
int bits = sizeof(int) * 8;
int i = 0;
int no = 0;
while(a[i] == -1)//this means a[i]'s bits are all set to 1, the numbers is not inside this 32 numbers section
{
no += bits;
i++;
}
return no + bits - Math.Log(~a[i], 2);//apply NOT (~) operator to a[i] to invert all bits, and get a number with only one bit set (2 at the power of something)
}
Example: (32 bit version) lets say that the missing number is 58. That means that the 26th bit (left to right) of the second integer is set to 0.
The first int is -1 (all bits are set) so, we go ahead for the second one and add to "no" the number 32. The second int is different from -1 (a bit is not set) so, by applying the NOT (~) operator to the number we get 64. The possible numbers are 2 at the power x and we may compute x by using log on base 2; in this case we get log2(64) = 6 => 32 + 32 - 6 = 58.
Hope this helps.
I think the easiest and possibly the most efficient solution would be to loop over all entries and use a bitset to remember which numbers are set, and then test for 0 bit. The entry with the 0 bit is the missing number.
This is not a search problem. The employer is wondering if you have a grasp of a checksum. You might need a binary or for loop or whatever if you were looking for multiple unique integers, but the question stipulates "one random empty slot." In this case we can use the stream sum. The condition: "The numbers are randomly added to the array" is meaningless without more detail. The question does not assume the array must start with the integer 1 and so tolerate with the offset start integer.
int[] test = {2,3,4,5,6,7,8,9,10, 12,13,14 };
/*get the missing integer*/
int max = test[test.length - 1];
int min = test[0];
int sum = Arrays.stream(test).sum();
int actual = (((max*(max+1))/2)-min+1);
//Find:
//the missing value
System.out.println(actual - sum);
//the slot
System.out.println(actual - sum - min);
Success time: 0.18 memory: 320576 signal:0
I found this beautiful solution here:
http://javaconceptoftheday.com/java-puzzle-interview-program-find-missing-number-in-an-array/
public class MissingNumberInArray
{
//Method to calculate sum of 'n' numbers
static int sumOfNnumbers(int n)
{
int sum = (n * (n+1))/ 2;
return sum;
}
//Method to calculate sum of all elements of array
static int sumOfElements(int[] array)
{
int sum = 0;
for (int i = 0; i < array.length; i++)
{
sum = sum + array[i];
}
return sum;
}
public static void main(String[] args)
{
int n = 8;
int[] a = {1, 4, 5, 3, 7, 8, 6};
//Step 1
int sumOfNnumbers = sumOfNnumbers(n);
//Step 2
int sumOfElements = sumOfElements(a);
//Step 3
int missingNumber = sumOfNnumbers - sumOfElements;
System.out.println("Missing Number is = "+missingNumber);
}
}
function solution($A) {
// code in PHP5.5
$n=count($A);
for($i=1;$i<=$n;$i++) {
if(!in_array($i,$A)) {
return (int)$i;
}
}
}
Finding the missing number from a series of numbers. IMP points to remember.
the array should be sorted..
the Function do not work on multiple missings.
the sequence must be an AP.
public int execute2(int[] array) {
int diff = Math.min(array[1]-array[0], array[2]-array[1]);
int min = 0, max = arr.length-1;
boolean missingNum = true;
while(min<max) {
int mid = (min + max) >>> 1;
int leftDiff = array[mid] - array[min];
if(leftDiff > diff * (mid - min)) {
if(mid-min == 1)
return (array[mid] + array[min])/2;
max = mid;
missingNum = false;
continue;
}
int rightDiff = array[max] - array[mid];
if(rightDiff > diff * (max - mid)) {
if(max-mid == 1)
return (array[max] + array[mid])/2;
min = mid;
missingNum = false;
continue;
}
if(missingNum)
break;
}
return -1;
}
One thing you could do is sort the numbers using quick sort for instance. Then use a for loop to iterate through the sorted array from 1 to 100. In each iteration, you compare the number in the array with your for loop increment, if you find that the index increment is not the same as the array value, you have found your missing number as well as the missing index.
Below is the solution for finding all the missing numbers from a given array:
public class FindMissingNumbers {
/**
* The function prints all the missing numbers from "n" consecutive numbers.
* The number of missing numbers is not given and all the numbers in the
* given array are assumed to be unique.
*
* A similar approach can be used to find all no-unique/ unique numbers from
* the given array
*
* #param n
* total count of numbers in the sequence
* #param numbers
* is an unsorted array of all the numbers from 1 - n with some
* numbers missing.
*
*/
public static void findMissingNumbers(int n, int[] numbers) {
if (n < 1) {
return;
}
byte[] bytes = new byte[n / 8];
int countOfMissingNumbers = n - numbers.length;
if (countOfMissingNumbers == 0) {
return;
}
for (int currentNumber : numbers) {
int byteIndex = (currentNumber - 1) / 8;
int bit = (currentNumber - byteIndex * 8) - 1;
// Update the "bit" in bytes[byteIndex]
int mask = 1 << bit;
bytes[byteIndex] |= mask;
}
for (int index = 0; index < bytes.length - 2; index++) {
if (bytes[index] != -128) {
for (int i = 0; i < 8; i++) {
if ((bytes[index] >> i & 1) == 0) {
System.out.println("Missing number: " + ((index * 8) + i + 1));
}
}
}
}
// Last byte
int loopTill = n % 8 == 0 ? 8 : n % 8;
for (int index = 0; index < loopTill; index++) {
if ((bytes[bytes.length - 1] >> index & 1) == 0) {
System.out.println("Missing number: " + (((bytes.length - 1) * 8) + index + 1));
}
}
}
public static void main(String[] args) {
List<Integer> arrayList = new ArrayList<Integer>();
int n = 128;
int m = 5;
for (int i = 1; i <= n; i++) {
arrayList.add(i);
}
Collections.shuffle(arrayList);
for (int i = 1; i <= 5; i++) {
System.out.println("Removing:" + arrayList.remove(i));
}
int[] array = new int[n - m];
for (int i = 0; i < (n - m); i++) {
array[i] = arrayList.get(i);
}
System.out.println("Array is: " + Arrays.toString(array));
findMissingNumbers(n, array);
}
}
Lets say you have n as 8, and our numbers range from 0-8 for this example
we can represent the binary representation of all 9 numbers as follows
0000
0001
0010
0011
0100
0101
0110
0111
1000
in the above sequence there is no missing numbers and in each column the number of zeros and ones match, however as soon as you remove 1 value lets say 3 we get a in balance in the number of 0's and 1's across the columns. If the number of 0's in a column is <= the number of 1's our missing number will have a 0 at this bit position, otherwise if the number of 0's > the number of 1's at this bit position then this bit position will be a 1. We test the bits left to right and at each iteration we throw away half of the array for the testing of the next bit, either the odd array values or the even array values are thrown away at each iteration depending on which bit we are deficient on.
The below solution is in C++
int getMissingNumber(vector<int>* input, int bitPos, const int startRange)
{
vector<int> zeros;
vector<int> ones;
int missingNumber=0;
//base case, assume empty array indicating start value of range is missing
if(input->size() == 0)
return startRange;
//if the bit position being tested is 0 add to the zero's vector
//otherwise to the ones vector
for(unsigned int i = 0; i<input->size(); i++)
{
int value = input->at(i);
if(getBit(value, bitPos) == 0)
zeros.push_back(value);
else
ones.push_back(value);
}
//throw away either the odd or even numbers and test
//the next bit position, build the missing number
//from right to left
if(zeros.size() <= ones.size())
{
//missing number is even
missingNumber = getMissingNumber(&zeros, bitPos+1, startRange);
missingNumber = (missingNumber << 1) | 0;
}
else
{
//missing number is odd
missingNumber = getMissingNumber(&ones, bitPos+1, startRange);
missingNumber = (missingNumber << 1) | 1;
}
return missingNumber;
}
At each iteration we reduce our input space by 2, i.e N, N/2,N/4 ... = O(log N), with space O(N)
//Test cases
[1] when missing number is range start
[2] when missing number is range end
[3] when missing number is odd
[4] when missing number is even
Solution With PHP $n = 100;
$n*($n+1)/2 - array_sum($array) = $missing_number
and array_search($missing_number) will give the index of missing number
Here program take time complexity is O(logn) and space complexity O(logn)
public class helper1 {
public static void main(String[] args) {
int a[] = {1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12};
int k = missing(a, 0, a.length);
System.out.println(k);
}
public static int missing(int[] a, int f, int l) {
int mid = (l + f) / 2;
//if first index reached last then no element found
if (a.length - 1 == f) {
System.out.println("missing not find ");
return 0;
}
//if mid with first found
if (mid == f) {
System.out.println(a[mid] + 1);
return a[mid] + 1;
}
if ((mid + 1) == a[mid])
return missing(a, mid, l);
else
return missing(a, f, mid);
}
}
public class MissingNumber {
public static void main(String[] args) {
int array[] = {1,2,3,4,6};
int x1 = getMissingNumber(array,6);
System.out.println("The Missing number is: "+x1);
}
private static int getMissingNumber(int[] array, int i) {
int acctualnumber =0;
int expectednumber = (i*(i+1)/2);
for (int j : array) {
acctualnumber = acctualnumber+j;
}
System.out.println(acctualnumber);
System.out.println(expectednumber);
return expectednumber-acctualnumber;
}
}
Use sum formula,
class Main {
// Function to ind missing number
static int getMissingNo (int a[], int n) {
int i, total;
total = (n+1)*(n+2)/2;
for ( i = 0; i< n; i++)
total -= a[i];
return total;
}
/* program to test above function */
public static void main(String args[]) {
int a[] = {1,2,4,5,6};
int miss = getMissingNo(a,5);
System.out.println(miss);
}
}
Reference http://www.geeksforgeeks.org/find-the-missing-number/
simple solution with test data :
class A{
public static void main(String[] args){
int[] array = new int[200];
for(int i=0;i<100;i++){
if(i != 51){
array[i] = i;
}
}
for(int i=100;i<200;i++){
array[i] = i;
}
int temp = 0;
for(int i=0;i<200;i++){
temp ^= array[i];
}
System.out.println(temp);
}
}
//Array is shorted and if writing in C/C++ think of XOR implementations in java as follows.
int num=-1;
for (int i=1; i<=100; i++){
num =2*i;
if(arr[num]==0){
System.out.println("index: "+i+" Array position: "+ num);
break;
}
else if(arr[num-1]==0){
System.out.println("index: "+i+ " Array position: "+ (num-1));
break;
}
}// use Rabbit and tortoise race, move the dangling index faster,
//learnt from Alogithimica, Ameerpet, hyderbad**
If the array is randomly filled, then at the best you can do a linear search in O(n) complexity. However, we could have improved the complexity to O(log n) by divide and conquer approach similar to quick sort as pointed by giri given that the numbers were in ascending/descending order.
This Program finds missing numbers
<?php
$arr_num=array("1","2","3","5","6");
$n=count($arr_num);
for($i=1;$i<=$n;$i++)
{
if(!in_array($i,$arr_num))
{
array_push($arr_num,$i);print_r($arr_num);exit;
}
}
?>
Now I'm now too sharp with the Big O notations but couldn't you also do something like (in Java)
for (int i = 0; i < numbers.length; i++) {
if(numbers[i] != i+1){
System.out.println(i+1);
}
}
where numbers is the array with your numbers from 1-100.
From my reading of the question it did not say when to write out the missing number.
Alternatively if you COULD throw the value of i+1 into another array and print that out after the iteration.
Of course it might not abide by the time and space rules. As I said. I have to strongly brush up on Big O.
========Simplest Solution for sorted Array===========
public int getMissingNumber(int[] sortedArray)
{
int missingNumber = 0;
int missingNumberIndex=0;
for (int i = 0; i < sortedArray.length; i++)
{
if (sortedArray[i] == 0)
{
missingNumber = (sortedArray[i + 1]) - 1;
missingNumberIndex=i;
System.out.println("missingNumberIndex: "+missingNumberIndex);
break;
}
}
return missingNumber;
}
Another homework question. A sequential search is the best that you can do. As for a Java solution, consider that an exercise for the reader. :P