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Are you looking to master bit manipulation and tackle interesting coding challenges? In this post, we’ll explore LeetCode Problem 3370: Smallest Number With All Set Bits . We’ll dive deep into the problem statement, break down a brute force approach, and finally discuss an optimized solution. If you’re preparing for technical interviews or just love solving algorithmic problems, this guide is for you! Problem Statement: Smallest Number With All Set Bits You are given a positive integer n . Your task is to find the smallest number x such that: x is greater than or equal to n . The binary representation of x consists only of set bits ( 1 s). Examples: Example 1: Input: n = 5 Output: 7 Explanation: The binary representation of 7 is 111 , which is the smallest number greater than or equal to 5 with all bits set. Example 2: Input: n = 10 Output: 15 Explanation: The binary representation of 15 is 1111 . Example 3: Input: n = 3 Output: 3 Explanation: The binary representation of 3 is...

LeetCode’s hard problems can seem daunting, especially when you’re just getting comfortable with coding. Tackling these problems isn’t about innate talent—it’s about persistence, learning, and applying the right strategies. In this blog, I’ll share actionable tips and techniques to help you solve LeetCode hard problems without getting stuck and make consistent progress in your problem-solving journey. Why Are Hard Problems Hard? LeetCode hard problems challenge your understanding of advanced data structures, algorithms, and optimization techniques. Here’s why they’re tough: They require deep problem-solving skills and out-of-the-box thinking. Many involve multiple layers of complexity . Edge cases and large inputs often test your algorithm’s efficiency. They push you to master topics like dynamic programming, graph traversal, and segment trees. However, with the right approach, you can break down even the most complex problems into manageable parts. Start with the Right Mindset Appro...

If you're new to solving coding problems on LeetCode, it can feel overwhelming. Where do you start? Which problems are suitable for beginners? Don’t worry! In this blog post, I’ll guide you through 10 beginner-friendly LeetCode questions that are perfect for getting started on your coding journey. These problems will help you build confidence, improve your problem-solving skills, and lay a solid foundation in data structures and algorithms. Why Start with Beginner-Friendly Problems? Before diving into advanced topics like dynamic programming or graph theory, it’s essential to: Build a strong foundation in basic programming concepts. Understand how to approach a coding problem methodically. Gain familiarity with LeetCode’s platform and its problem structure. The following problems are simple yet impactful, designed to introduce you to common techniques like loops, arrays, strings, and basic math operations. 10 Beginner-Friendly LeetCode Problems 1. Two Sum (Easy) Problem Link : Two...

 his problem tests our understanding of efficient array traversal techniques and highlights the power of binary search when working with sorted arrays. By the end of this post, you’ll have a clear, optimized solution in C++ that avoids Time Limit Exceeded (TLE) errors and uses two-pointer and binary search techniques . Let’s get started by breaking down the problem and exploring a step-by-step approach to a performant solution. Problem Statement Given: An integer array nums of size n . Two integers, lower and upper . Our goal is to count all pairs (i, j) where: 0 <= i < j < n , and lower <= nums[i] + nums[j] <= upper . Example Walkthrough Let's clarify with an example: Example 1 Input: nums = [0, 1, 7, 4, 4, 5] , lower = 3 , upper = 6 Output: 6 Explanation: Valid pairs that satisfy the condition are (0,3) , (0,4) , (0,5) , (1,3) , (1,4) , and (1,5) . Example 2 Input: nums = [1, 7, 9, 2, 5] , lower = 11 , upper = 11 Output: 1 Explanation: There is only one valid p...

This problem involves maximizing the XOR operation across multiple queries, making it an interesting and challenging problem. In this blog post, we’ll walk through how to approach LeetCode 1829 , starting with a brute-force solution and gradually optimizing it for better performance. We’ll break down the problem, explain the brute-force method, and then introduce an optimized approach that significantly improves efficiency. Problem You are given an array of non-negative integers ( nums ) sorted in ascending order and an integer maximumBit . Your task is to perform n queries (where n is the size of the array) and for each query, find a non-negative integer k that maximizes the XOR result when combined with all elements of the array up to that point. For each query: You XOR all elements of the array up to the current query. You find a k (where 0 <= k < 2^maximumBit ) such that the result of XOR-ing all elements with k is maximized. After each query, the last element is remov...

 In a Binary Search Tree (BST), finding the minimum absolute difference between the values of any two distinct nodes is a classic problem in tree data structure algorithms. This problem is a natural extension of the concept of BST properties and is useful in scenarios such as range queries, nearest neighbor searches, and optimization problems. In this blog post, we’ll walk through the approach to solve the problem of finding the minimum distance between two nodes in a BST. We will break down the solution, discuss different strategies, and demonstrate an optimized approach to solve the problem efficiently. Problem Statement: Minimum Distance Between BST Nodes (LeetCode Problem 783) Problem Description: You are given the root of a Binary Search Tree (BST). Your task is to find the minimum absolute difference between the values of any two distinct nodes in the tree. For example: Input 1: root = [4, 2, 6, 1, 3] Output: 1 The nodes with values 2 and 3 have the smallest absolute differ...

When working with Binary Search Trees (BSTs), one of the most common operations is searching for a node with a specific value. This operation is fundamental to many algorithms and data structures that rely on trees. In this blog post, we will explore how to search for a node in a Binary Search Tree (BST), explain the step-by-step approach, and optimize it for better performance. What is a Binary Search Tree (BST)? Before diving into the search algorithm, let's quickly define a Binary Search Tree (BST) . A BST is a type of binary tree where each node has at most two children: a left child and a right child. The key property of a BST is that for each node: All values in the left subtree are less than or equal to the node’s value. All values in the right subtree are greater than or equal to the node’s value. This property allows for efficient searching, as we can eliminate half of the remaining search space at each step, just like binary search in an array. The Problem: Search in a Bi...

In this LeetCode problem, we're tasked with transforming a given binary string into a "beautiful" string. A beautiful string is one that can be partitioned into substrings, each containing only 0s or 1s and having an even length. To achieve this beauty, we're allowed to change any character in the string to either 0 or 1. The challenge lies in determining the minimum number of such changes required. Intuition and Approach To solve this problem efficiently, we can employ a greedy approach. We'll iterate through the string, keeping track of the current character and the count of consecutive occurrences of that character. Iterate and Count: Start with the first character and initialize a count to 1. As we traverse the string, we increment the count if the current character matches the previous one. If the current character is different, we've encountered a new substring. Handle Odd-Length Substrings: If the current count is odd, we'll need to make...

String manipulation is a common task in programming, especially when it comes to optimizing data storage or transmission. One fascinating challenge is the "String Compression III" problem from LeetCode, which requires you to compress a string according to specific rules. In this post, we’ll explore the problem statement, discuss a practical solution, and delve into the intricacies of the algorithm. Whether you’re preparing for coding interviews or simply want to enhance your problem-solving skills, this guide will provide valuable insights. Problem Statement You are given a string called word , and your task is to compress it using a specific algorithm. The rules for compression are as follows: Begin with an empty string comp . While word is not empty, remove a maximum length prefix made of a single character c , which can repeat at most 9 times. Append the length of the prefix followed by the character c to comp . Continue this process until all characters in word are pro...