There are two sorted arrays A and B of size n each, write a program to find the median of the array obtained after merging both the arrays(i.e., an array of length 2n which is even). The median of a sorted array of size n is defined as the middle element when n is odd and the average of the middle two elements when n is even.

Given an array A[] of integers, find out the maximum difference between any two elements such that the larger element appears after, the smaller element. In other words, we need to find max(A[j] - A[i]), where A[j] > A[i] and j > i.

Learning analysis of recursion is critical to understand the time complexity analysis of recursive algorithms. We will discuss these concepts related to the recursion analysis: Recurrence relations of recursive algorithms, steps to analyze the time complexity of recursion, Recursion tree method, and master theorem to analyze divide and conquer algorithms.

Given an array and a positive integer k, write a program to find the kth smallest element in the array. One of the famous problems to learn a quick-select approach that helps optimize time complexity to O(n) average.

You are given an array X[] consisting of n elements, write a program to find majority element in an array i..e return the number which appears more than n/2 times. You may assume that the array is non-empty and the majority element always exists in the array. A majority element is an element that appears more than n/2 times, so there is at most one such element.

Given an array X[] with n elements, we need to write a program to find the largest contiguous subarray sum. A subarray of array X[] of length n is a contiguous segment from X[i] through X[j] where 0<= i <= j <= n. Kadane algorithm idea is intuitive, using a single loop and few variables to solve the problem. We can use a similar idea to solve other coding problems.

The binary search is one of the fastest search algorithms, which searches a value in the sorted array in an O(logn) time complexity. Here we search a value using divide and conquer by repeatedly dividing the search interval in half. Problem statement: Given a sorted array X[] of n elements, search a given element key in X[]. If the key exists, then return its index in the sorted array. Otherwise, return -1.

Quicksort is often the best practical choice for sorting because it works remarkably efficiently on average O(nlogn) time complexity. It is also one of the best algorithms to learn problem-solving using recursion and divide and conquer approach. In this blog, we have covered: 1) How quick sort works recursively? 2) Choosing a correct pivot value in the partition algorithm 3) Best, worst, and average-case time complexity analysis 4) Space complexity and essential properties of the quick sort. Explore and Enjoy!

Given an array X[] of size n, we need to find the maximum and minimum element present in the array. This coding problem has been asked during facebook and microsoft interview.

Merge sort is one of the fastest comparison-based sorting algorithms, which works on the principle of the divide and conquer approach. The worst and best case time complexity of merge sort is O(nlogn), and space complexity is O(n). It is also the best algorithm for sorting linked lists.