Software Engineering Get Started with Flutter App Development We have explored the basic steps to create a simple application using Flutter which is a mobile application development framework.
Algorithms Find the longest increasing subsequence using Fenwick Tree You are given an array of length N. You have to find the longest increasing subsequence from the given array. We will try to solve this problem using Fenwick Tree which will take O(N logN) time complexity.
Algorithms Find the Longest Increasing Odd Even Subsequence In this problem, we have formulated an algorithm to find the Longest Increasing Odd Even Subsequence. Brute force approach takes exponential time O(2^N) but a Dynamic Programming approach takes O(N^2) time.
Algorithms Find the Longest Common Increasing Subsequence In this problem, we have formulated an algorithm to find the Longest Common Increasing Subsequence. Brute force approach takes exponential time but a Dynamic Programming approach takes O(N^2) time.
Algorithms Minimum insertions to make the frequency of each character unique We developed a string algorithm to find out the minimum number of insertions to be made in a string so that the frequency of each character is unique
Algorithms Minimum deletions to make frequency of each character unique The problem we will solve is that given a string s, we need to find the minimum number of characters to be deleted from s so that the frequency of each character in the string is unique.
Algorithms Minimum number of Fibonacci terms with sum equal to a given number N Given a number N, we need to find the minimum number of fibonacci numbers required that sums up to a given number N. Note a fibonacci number can be repeated
Algorithms Number of ways to represent a number N as sum of K Fibonacci terms The problem we will solve is that given a number N and K, we need to find the numbers of ways we can represent N as a sum of K fibonacci numbers. We will solve this problem efficiently using a recursive technique in O(N).
Algorithms Break a number in 3 parts (n/2, n/3, n/4) recursively to get maximum sum Given a number n, we can divide it in only three parts n/2, n/3 and n/4 recursively and find the maximum sum of the 3 parts. This can be solved in linear time using Dynamic Programming
