Skip to main content

The Missing Duplicate Number - Easy

Problem : Given an array of integers of odd length such that each element is repeated twice except for one. Find the non-repeating element. Eg. For array [3,2,1,5,1,2,3] the solution is 5.

A tiny variation of the question which doesn't really change the answer is as follows.

Problem : Given an array of integers of odd length such that each element is repeated even number of times except for one element. Eg. For array [1,5,5,1,2,2,4,4,4] the solution is 4.

Let's begin with the naive solution, we use a Hash Table to keep track of the frequencies of my each element in the array. Finally, we find the element in the Hash Table with the odd frequency which is the solution required. The space and time complexity is O(n).

We'll try and reduce the space complexity. This is one of those questions which will be solved using our trusty Xor operator. You should know X ^ X = 0 and also Xor is Commutative and associative. ie X ^ Y is Y ^ X.

Therefore, (X ^ Y) ^ X is the same as (X ^ X) ^ Y => 0 ^ Y => Y

So if we run an Xor operation over the array elements, all the even repeated elements cancel each other and the odd repeated element remains. I won't post the code. Here's another explanation I posted on Stackoverflow.

Comments

Popular posts from this blog

Find Increasing Triplet Subsequence - Medium

Problem - Given an integer array A[1..n], find an instance of i,j,k where 0 < i < j < k <= n and A[i] < A[j] < A[k]. Let's start with the obvious solution, bruteforce every three element combination until we find a solution. Let's try to reduce this by searching left and right for each element, we search left for an element smaller than the current one, towards the right an element greater than the current one. When we find an element that has both such elements, we found the solution. The complexity of this solution is O(n^2). To reduce this even further, One can simply apply the longest increasing subsequence algorithm and break when we get an LIS of length 3. But the best algorithm that can find an LIS is O(nlogn) with O( n ) space . An O(nlogn) algorithm seems like an overkill! Can this be done in linear time? The Algorithm: We iterate over the array and keep track of two things. The minimum value iterated over (min) The minimum increa...

Merge k-sorted lists - Medium

Problem - Given k-sorted lists, merge them into a single sorted list. A daft way of doing this would be to copy all the list into a new array and sorting the new array. ie O(n log(n)) The naive method would be to simply perform k-way merge similar to the auxiliary method in Merge Sort. But that is reduces the problem to a minimum selection from a list of k-elements. The Complexity of this algorithm is an abysmal O(nk). Here's how it looks in Python. We maintain an additional array called Index[1..k] to maintain the head of each list. We improve upon this by optimizing the minimum selection process by using a familiar data structure, the Heap! Using a MinHeap, we extract the minimum element from a list and then push the next element from the same list into the heap, until all the list get exhausted. This reduces the Time complexity to O(nlogk) since for each element we perform O(logk) operations on the heap. An important implementation detail is we need to keep track ...

2SUM - Medium

Problem : Given an array with distinct elements. Find out if there exists an instance where sum of two distinct elements is equal to a given constant K. Say, A = [1,2,3,4], K = 7. Output should be 3,4 The naive solution for this problem is pretty trivial. Brute force every pair-sum and check if the sum is K. You could be a little smart about this. You initially sort the array O(nlogn) and iterate the array pairwise and quit when the sum exceeds K, you can quit the inner loop since the array is sorted the sum can only increase. For the rest of the article, I'll be assuming K = 0, this doesn't affect the solutions in any way and it's trivial to introduce a new variable into the code without major changes. Here's the code, should be easy to follow... I'll be using Java today. Now that the naive solution is out of the way, let's try and do some clever stuff. So notice we can afford to sort the array since the brute solution was already O(n^2) (which ...