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 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 difference

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

When it comes to data structures in computer science, binary trees and linked lists are fundamental concepts that often come into play. In this post, we will explore an intriguing problem: converting a binary tree into a doubly linked list (DLL). This task may seem daunting, but with the right approach, we can achieve it seamlessly. Let's dive into the details! Understanding the Problem What is a Binary Tree? A binary tree is a hierarchical structure in which each node has at most two children referred to as the left child and the right child. This structure is crucial for organizing data hierarchically, facilitating efficient searching, inserting, and deleting operations. What is a Doubly Linked List? A doubly linked list (DLL) is a linear data structure consisting of nodes, where each node contains three components: a data field and two pointers. One pointer points to the next node in the sequence (next), while the other points to the previous node (prev). This bidirectional natu

String manipulation is one of the most commonly tested topics in coding interviews, and LeetCode's Problem 1593: Split a String Into the Max Number of Unique Substrings gives us an interesting challenge. The goal is to split a string into as many unique substrings as possible. In this blog post, we’ll go step by step to understand the problem, come up with an efficient solution, and analyze its time and space complexity. Problem Statement Given a string s , your task is to split it into the maximum number of unique substrings . You can break the string into any list of non-empty substrings as long as the concatenation of all substrings forms the original string, and all the substrings in the split are unique . A substring is defined as a contiguous sequence of characters within a string. Example 1: Input : s = "ababccc" Output : 5 Explanation : One way to split the string is ["a", "b", "ab", "c", "cc"] . Note that each