Bharath_Writes

When solving problems efficiently, choosing the right strategy makes all the difference. A common comparison in programming is Binary Search vs. Brute Force (or the Naïve approach ). While brute force might seem straightforward, it often becomes impractical for large inputs. Binary search, on the other hand, is a game-changer. Let's break it down in simple terms. 1. Understanding Brute Force (Naïve Approach) Imagine you have a book with 1,000 pages and you are looking for a particular word. If you start from page 1 and check each page one by one, that’s the brute force approach . This method works but is slow because you have to go through every single page before finding what you need. In coding, brute force means trying all possible solutions until you find the correct one. While this approach is easy to understand, it is very slow when the number of possibilities is huge. Example: Car Repair Problem Let’s say you have mechanics who can repair cars, and you want to find ou...

Imagine a street with several houses, each containing a certain amount of money. A robber wants to steal money, but there’s a catch – he cannot rob two adjacent houses. The goal is to find the minimum possible maximum amount he needs to be capable of stealing while ensuring he robs at least k houses. Understanding the Problem We are given: nums: An array where nums[i] represents the money in the i-th house. k: The minimum number of houses the robber must steal from. We need to find the smallest possible "capability", meaning the highest amount the robber steals from any house in his plan. How Do We Solve This Efficiently? Since nums can be large (up to 10^5 houses), checking every combination would be too slow. Instead, we use Binary Search to efficiently find the answer. Steps to Solve Using Binary Search Set the Search Range The lowest capability (l) is the minimum house value. The highest capability (r) is the maximum house value. Binary Search for the Minimum Capability W...

Introduction Have you ever encountered a problem where you need to update multiple ranges in an array efficiently? Updating each element one by one can be too slow , especially for large datasets. This is where the Difference Array Technique comes in! It allows us to perform range updates in O(1) time per update and compute the final array efficiently. In this blog, we’ll break it down with step-by-step explanations and visuals to make it super easy to understand. Understanding the Problem Let’s say we have an array: A = [ 0 , 0 , 0 , 0 , 0 ] A = [0, 0, 0, 0, 0] A = [ 0 , 0 , 0 , 0 , 0 ] We want to perform multiple operations like: 1️⃣ Add 10 to indices [1 to 3] 2️⃣ Add 5 to indices [2 to 4] 3️⃣ Add 20 to indices [0 to 2] A naive approach would be to loop through each range and update elements one by one , which takes O(n) per operation . For large arrays and multiple queries, this becomes very inefficient ! 🚨 What is the Difference Array? Instead of updating each ele...