Road2Geeks

Problem Overview The task is to find the kth largest element in an unsorted array. Although sorting the array would work, it requires O(n log n) time, which is inefficient for this problem. Heap-based solutions allow us to optimize this process by partially ordering elements instead of fully sorting the array. Approach 1: Min-Heap of Size k This approach maintains a min-heap that stores only the k largest elements seen so far. Key Idea Use a min-heap Keep heap size limited to k The smallest element in the heap is the kth largest overall Algorithm Steps Initialize an empty min-heap Traverse the array If heap size is less than k , push the element Otherwise, push the new element and remove the smallest one After traversal, the heap root is the answer Python Code import heapq class Solution: def findKthLargest(self, nums: List[int], k: int) -> int: min_heap = [] for num in nums: if len(min_heap) < k:...

Clone Graph Problem – DFS Based Solution in Python The Clone Graph problem is a classic graph traversal question where the goal is to create a deep copy of a connected undirected graph. Each node contains a value and a list of its neighboring nodes. The challenge lies in correctly handling cycles and repeated references without creating duplicate nodes or falling into infinite recursion. Problem Overview You are given a reference to a node in a connected undirected graph. Each node has: An integer value val A list of neighboring nodes neighbors Your task is to return a deep copy of the entire graph. Key Insight Graphs can contain cycles, meaning a node can be revisited during traversal. To avoid cloning the same node multiple times, we use a hash map to keep track of already cloned nodes. This solution uses Depth First Search (DFS) with a dictionary called visited : Key → Original node Value → Cloned node DFS-Based Cloning Strate...

Binary Tree Level Order Traversal — Python Solution Level Order Traversal is one of the fundamental tree traversal techniques where we visit every node of a binary tree level by level, starting from the root and moving left-to-right within each level. It’s widely used in breadth-first search (BFS) patterns and is a staple in algorithm interviews. :contentReference[oaicite:0]{index=0} 💡 What is Level Order Traversal? Given the root of a binary tree, the goal is to return a nested list where each sublist represents all node values at that specific level of the tree. For example, for the binary tree: Input: root = [3,9,20,null,null,15,7] 3 / \ 9 20 / \ 15 7 The expected traversal output is: [ [3], [9,20], [15,7] ] 📌 Idea Behind the App...

Greedy Pattern: Erase Overlapping Intervals (LeetCode 435) Some problems quietly test whether you recognize a classic greedy pattern . Erase Overlapping Intervals is one of them. At first glance, it feels like an interval-merging problem. But once you pause and think — the real objective becomes clear: Remove the minimum number of intervals so that the remaining intervals do not overlap. Problem Restatement Given an array of intervals where intervals[i] = [start, end] , return the minimum number of intervals you need to remove to make the rest non-overlapping. Key Insight (Pattern Recognition) Instead of asking: Which intervals should I remove?

Capacity To Ship Packages Within D Days – Binary Search on Answer This problem is a classic example of the Binary Search on Answer pattern. Instead of searching within the array, we search over the range of possible ship capacities and determine the minimum capacity required to ship all packages within the given number of days. Problem Intuition We are given a list of package weights that must be shipped in the same order within a fixed number of days. Each day, the ship has a fixed capacity and we load packages sequentially until the capacity is exceeded, after which shipping continues the next day. The key observation is that: If a certain ship capacity works, then any larger capacity will also work. If a capacity does not work, then any smaller capacity will also fail. This monotonic behavior allows us to apply binary search efficiently. Search ...

Binary Search on Answer – Smallest Divisor Given a Threshold Binary Search on Answer is a powerful problem-solving pattern where instead of searching within an array, we perform binary search over the range of possible answers . LeetCode problem 1283 – Smallest Divisor Given a Threshold is a classic example of this pattern. 🔍 Problem Intuition We are given an array of positive integers and a threshold. For any chosen divisor d , we divide each element by d , round it up using ceil , and sum the results. Our goal is to find the smallest possible divisor such that this sum does not exceed the given threshold. 💡 Key Observation If the divisor is small → the sum becomes large If the divisor is large → the sum becomes small This creates a monotonic condition , making it a perfect candidate for binary search on the answer. ⚠️ Important Edge Case The divisor can never be 0 . S...

Problem Overview The Product of Array Except Self is a classic problem that tests your understanding of array traversal and optimization. The task is simple to state but tricky to implement efficiently. Given an integer array nums , you need to return an array such that each element at index i is equal to the product of all the elements in nums except nums[i] . The challenge is that: Division is not allowed The solution must run in O(n) time Initial Thoughts At first glance, it feels natural to compute the total product of the array and divide it by the current element. However, this approach fails because division is forbidden and handling zeroes becomes messy. This pushed me to think differently — instead of excluding the current element, why not multiply everything around it? That’s where the prefix and suffix product pattern comes in. Key Insight: Prefix & Suffix Products For every index i : Prefix product → product of all elements to t...

Minimum Window Substring – Sliding Window Approach While solving the Minimum Window Substring problem, I initially struggled to manage window validity correctly. The key challenge was ensuring that the current window contains all characters of the target string with correct frequency , while still keeping the window as small as possible. Problem Intuition The problem is a classic example of a variable-size sliding window . We expand the window to the right until it becomes valid, and once valid, we try to shrink it from the left to find the minimum-length substring. To efficiently check window validity, instead of rechecking all characters every time, we maintain a frequency map and a counter that tracks how many characters are still required. Approach Create a frequency map of the target string. Use two pointers ( l and r ) to represent the sliding window. Expand the window by moving r . Decrease the required count only when a needed character is found. ...

Majority Element in an Array Given an integer array nums of size n , the task is to find the majority element . A majority element is defined as the element that appears more than ⌊n / 2⌋ times in the array. It is guaranteed that the majority element always exists. Problem Example Input: nums = [3, 2, 3] Output: 3 Input: nums = [2,2,1,1,1,2,2] Output: 2 Approach 1: Using Counter (Easy to Understand) A straightforward approach is to count the frequency of each element and return the one with the maximum count. Python Code from collections import Counter class Solution: def majorityElement(self, nums): freq = Counter(nums) max_count = max(freq.values()) for key in freq: if freq[key] == max_count: return key Complexity Analysis Time Complexity: O(n) Space Complexity: O(n) Although this solution is simple and readable, it us...

Python Project: AI-Based Assignment Completer A small suite of Python scripts that generate C programs from prompts, create outputs and collect code into a .docx file. 1. File 1 — generator (generate C files) Notes: Replace the API key placeholder on the line where openai.api_key is set with your own key, or set openai.api_key elsewhere in your environment. Create a prompt.txt file in the same directory with one question per line. Create a folder named programms in the same directory — generated C files will be placed there. The Python code (unchanged): import os import openai class Ai: number=0 def automatedquery(self, query): '''this f(x) returns the responce generated by cahtgpt''' openai.api_key = "Your Api Key Here!" response = openai.Completion.create( model="text-davinci-003", prompt=query, ...