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...

Dijkstra's algorithm - Part 1 - Tutorial

This will be a 3 Part series of posts where I will be implementing the Dijkstra's Shortest Path algorithm in Python. The three parts will be 1) Representing the Graph 2) Priority Queue 3) The Algorithm To represent a graph we'll be using an  Adjacency List . Another alternative is using an Adjacency Matrix, but for a sparse graph an Adjacency List is more efficient. Adjacency List An Adjacency List is usually represented as a HashTable (or an Array) where an entry at `u` contains a Linked List. The Linked List contains `v` and optionally another parameter `w`. Here `u` and `v` are node(or vertex) labels and `w` is the weight of the edge. By Traversing the linked list we obtain the immediate neighbours of `u`. Visually, it looks like this. For implementing this in Python, we'll be using the dict()  for the main HashTable. For the Linked List we can use a list of 2 sized tuples (v,w).  Sidenote: Instead of a list of tuples, you can use a dict(), ...

Find the Quadruplets - Hard

Problem - Given 4 arrays A,B,C,D. Find out if there exists an instance where A[i] + B[j] + C[k] + D[l] = 0 Like the Find the Triple problem, we're going to develop 4 algorithms to solve this. Starting with the naive O(n^4) solution. Then we proceed to eliminate the inner-most loop with a Binary Search, reducing the complexity to O(n^3 logn) Now, we replace the last 2 loops with the left-right traversal we did in the previous 3 posts. Now the complexity is O(n^3). Finally, we reduce the complexity to O(n^2 logn) at the cost of O(n^2) Space Complexity. We store every combination of A[i] + B[j] and store it in AB[]. Similarly we make CD[] out of C[i] + D[j]. So, AB = A x B CD = C x D We then sort AB and CD (which costs O(n^2 log(n^2)) ~ O(n^2 logn) ) and then run a left-right linear Algorithm on AB and CD. (Note : Their size is of the order O(n^2)) So the overall complexity is due to sorting the large array of size n^2. which is O(n^2 logn).