Object Oriented Programming

GATE

Data Structures

Data Structures in Python

Power BI

Data Science

Django

Data Structure and Algorithm

Python

Recursion

Arrays

Searching

Sorting

String

Linked List

Data Structures in C

Algebra

Maths

Strings

C++ Tricks

System Design

Theory of Computation

Computer Organization and Architecture

Operating System

Engineering Mathematics

Database

Digital Logic

Computer Networks

GATE 2022

Java

GeeksforGeeks School

Hashing

Competitive Programming

Problem of the Day

School

HTML

Javascript

Bootstrap

Web Development

Android

Excel

Microsoft Office

Machine Learning

Resume Builder

Miscellaneous

Gblog

TechTips

React

Advanced JavaScript

DSA Self Paced

CS-subjects

Data Structures and Algorithms

Operating Systems

C Language

DBMS

Basics of CPP

Data Structures ( Basics )

Object Oriented Design

Dynamic Programming

Data Interpretation and Logical Reasoning

Algorithms

Core Java

RESTful APIs

Spring Basics

CPP-Tricks

Basics of Java

Aptitude

GATE YT

Flutter

Application Development

Blockchain

GFG SDE Sheet

SDE Sheet

Deep Learning

Go Programming

Placement

C++ Programs

Data Visualisation

DevOps

Docker

Queue

Matrix

Trie

Stack

Graph

Tree

Magic Numbers

Divide and Conquer

React Native

React JS

Interview-Questions

Advanced Java

cloud-computing

income tax

Artificial Intelligence

Programming Languages

Computer Graphics

Cyber Security

Project Management

DSA / Placements

DA / Data Science & AI

Development

DevOps Course

All Courses

Courses

ML & Data Science

CS Subjects

Software and Tools

School Learning

Tutorials

Interview Corner

Practice DSA

GfG 160

Interview Prep

This video of our DSA Course delves into another crucial data structure - the Queue Data Structure.Discover the working of First In, First Out (FIFO) principle and explore the characteristics that define the Queue data structure. From essential enqueue and dequeue operations to peek(), front(), rear(), isFull(), and isEmpty(), &nbsp;we cover all the Queue Operations!🔍 Gain a comprehensive understanding of different types of Queues, including Input Restricted Queue, Output Restricted Queue, Circular Queue, Double Ended Queue, and Priority Queue. 📊 Learn how we can represent Queues using Arrays and Linked Lists.🌐 Immerse yourself in real-world applications of Queue data structures, illustrated with live examples that showcase their importance in various scenarios. Whether it's managing tasks, processing data, or optimizing system resources, Queues play a crucial role!

WHAT IS QUEUE? | Queue Data Structures | DSA Course

There are two conventions to define the height of a Binary Tree&nbsp; 1) Number of nodes on the longest path from the root to the deepest node.&nbsp; 2) Number of edges on the longest path from the root to the deepest node.Iterative Method to find Height of Binary Tree: <a href="https://www.geeksforgeeks.org/iterative-method-to-find-height-of-binary-tree/">https://www.geeksforgeeks.org/iterative-method-to-find-height-of-binary-tree/</a>

Iterative Method to find Height of Binary Tree

Given a Binary tree and a key in the binary tree, find the node right to the given key. If there is no node on right side, then return NULL. Expected time complexity is O(n) where n is the number of nodes in the given binary tree.For example, consider the following Binary Tree. Output for 2 is 6, output for 4 is 5. Output for 10, 6 and 5 is NULL.&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;10 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; / &nbsp; &nbsp; &nbsp;\ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;2 &nbsp; &nbsp; &nbsp; &nbsp; 6 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; / &nbsp; \ &nbsp; &nbsp; &nbsp; &nbsp; \&nbsp; &nbsp; &nbsp; 8 &nbsp; &nbsp; &nbsp;4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;5Find next right node of a given key: <a href="https://www.geeksforgeeks.org/find-next-right-node-of-a-given-key/">https://www.geeksforgeeks.org/find-next-right-node-of-a-given-key/</a>

Find next right node of a given key

Given a Binary Tree having positive and negative nodes, the task is to find the maximum sum level in it.Examples:Input : &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 4 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;/ &nbsp; \ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 2 &nbsp; &nbsp;-5 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;/ \ &nbsp; &nbsp;/\ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;-1 &nbsp; 3 -2 &nbsp;6 Output: 6 Explanation : Sum of all nodes of 0'th level is 4 Sum of all nodes of 1'th level is -3 Sum of all nodes of 0'th level is 6 Hence maximum sum is 6Find maximum level sum in Binary Tree: <a href="https://www.geeksforgeeks.org/find-level-maximum-sum-binary-tree/">https://www.geeksforgeeks.org/find-level-maximum-sum-binary-tree/</a>

Find maximum level sum in Binary Tree

we introduced Queue and discussed array implementation. In this post, linked list implementation is discussed. The following two main operations must be implemented efficiently. In a Queue data structure, we maintain two pointers, front and rear. The front points the first item of queue and rear points to last item.enQueue() This operation adds a new node after rear and moves rear to the next node.deQueue() This operation removes the front node and moves front to the next node. Queue _ Set 2 (Linked List Implementation) : <a href="https://www.geeksforgeeks.org/queue-linked-list-implementation/">https://www.geeksforgeeks.org/queue-linked-list-implementation/</a>

Queue | Set 2 (Linked List Implementation)

a different approach using two queues is discussed. We can insert the first level in first queue and print it and while popping from the first queue insert its left and right nodes into the second queue. Now start printing the second queue and before popping insert its left and right child nodes into the first queue. Continue this process till both the queues become empty.Level order traversal line by line &nbsp;Set 2 (Using Two Queues): <a href="https://www.geeksforgeeks.org/level-order-traversal-line-line-set-2-using-two-queues/">https://www.geeksforgeeks.org/level-order-traversal-line-line-set-2-using-two-queues/</a>

Level order traversal line by line | Set 2 (Using Two Queues)

Welcome to the daily solving of our PROBLEM OF THE DAY with Karan Mashru We will discuss the entire problem step-by-step and work towards developing an optimized solution. This will not only help you brush up on your concepts of Sliding-window but also build up problem-solving skills. Given an array arr[] and an integer k. Find the maximum for each and every contiguous subarray of size k.Examples:Input: arr[] = [1, 2, 3, 1, 4, 5, 2, 3, 6], k = 3
Output: 3 3 4 5 5 5 6 
Explanation: 
1st contiguous subarray = [1 2 3] max = 3
2nd contiguous subarray = [2 3 1] max = 3
3rd contiguous subarray = [3 1 4] max = 4
4th contiguous subarray = [1 4 5] max = 5
5th contiguous subarray = [4 5 2] max = 5
6th contiguous subarray = [5 2 3] max = 5
7th contiguous subarray = [2 3 6] max = 6Give the problem a try before going through the video. All the best!!! Problem Link: <a href="https://practice.geeksforgeeks.org/problems/maximum-of-all-subarrays-of-size-k3101/1">https://practice.geeksforgeeks.org/problems/maximum-of-all-subarrays-of-size-k3101/1</a>

PROBLEM OF THE DAY : 27/09/2024 | K Sized Subarray Maximum

Welcome to the daily solving of our PROBLEM OF THE DAY with Yash Dwivedi We will discuss the entire problem step-by-step and work towards developing an optimized solution. This will not only help you brush up on your concepts of Heap but also build up problem-solving skills. Given an array arr containing the lengths of the different ropes, we need to connect these ropes to form one rope. The cost to connect two ropes is equal to sum of their lengths. The task is to connect the ropes with minimum cost. &nbsp;Examples:Input: arr[] = [4, 3, 2, 6]
Output: 29
Explanation: We can connect the ropes in following ways.
1) First connect ropes of lengths 2 and 3. Which makes the array [4, 5, 6]. Cost of this operation 2 + 3 = 5. 
2) Now connect ropes of lengths 4 and 5. Which makes the array [9, 6]. Cost of this operation 4 + 5 = 9.
3) Finally connect the two ropes and all ropes have connected. Cost of this operation 9 + 6 =15
Total cost for connecting all ropes is 5 + 9 + 15 = 29. This is the optimized cost for connecting ropes. 
Other ways of connecting ropes would always have same or more cost. For example, if we connect 4 and 6 first (we get three rope of 3, 2 and 10), then connect 10 and 3 (we gettwo rope of 13 and 2). Finally we connect 13 and 2. Total cost in this way is 10 + 13 + 15 = 38.Give the problem a try before going through the video. All the best!!! Problem Link: <a href="https://practice.geeksforgeeks.org/problems/minimum-cost-of-ropes-1587115620/1">https://practice.geeksforgeeks.org/problems/minimum-cost-of-ropes-1587115620/1</a>

PROBLEM OF THE DAY : 11/09/2024 | Minimum Cost of Ropes

<h2>Sliding Window Maximum | Maximum of All Subarrays of Size k</h2>In this guide, we’ll explore the sliding window maximum problem, which involves finding the maximum value in every subarray of size k in a given array. This problem is widely used in scenarios like analyzing time series data, optimizing algorithms, and solving computational geometry problems. By the end of this tutorial, you will understand how to solve this problem efficiently using the sliding window technique.<h2>Problem Definition</h2>Given an array of integers and an integer k, the task is to find the maximum of every subarray of size k in the array.<h3>Example:</h3><ul><li>Input: arr = [1, 3, -1, -3, 5, 3, 6, 7], k = 3</li><li>Output: [3, 3, 5, 5, 6, 7]</li></ul>Here, for every subarray of size k, we compute the maximum:<ul><li>Subarray [1, 3, -1] → Max = 3</li><li>Subarray [3, -1, -3] → Max = 3</li><li>Subarray [-1, -3, 5] → Max = 5</li><li>Subarray [-3, 5, 3] → Max = 5</li><li>Subarray [5, 3, 6] → Max = 6</li><li>Subarray [3, 6, 7] → Max = 7</li></ul><h2>Key Points Covered:</h2>Brute-Force Approach:<ul><li>A simple approach is to iterate over all subarrays of size k and compute the maximum value for each.</li><li>Drawback: This method has a time complexity of O(n×k)O(n \times k)O(n×k), where n is the size of the array. For large arrays, this approach becomes inefficient.</li></ul>Optimized Approach Using Deque:<ul><li>The sliding window maximum problem can be solved more efficiently using a deque (double-ended queue). The deque helps maintain the maximum element within the current sliding window in O(n)O(n)O(n) time.</li><li>A deque is used to store indices of array elements, ensuring that the maximum element is always at the front of the deque, while irrelevant elements (those outside the current window) are removed from the deque.</li></ul><h3>Steps to Solve Using Deque:</h3>Initialize an Empty Deque:<ul><li>The deque will store indices of the array elements, ensuring that the maximum of each window can be accessed in constant time.</li></ul>Traverse Through the Array:<ul><li>For each element in the array, perform the following operations:<ul><li>Remove elements from the deque that are no longer within the current window of size k.</li><li>Remove elements from the deque that are smaller than the current element, as they cannot be the maximum for the current window.</li><li>Add the index of the current element to the deque.</li></ul></li></ul>Track Maximum Elements:<ul><li>After processing each window (i.e., once the size of the deque reaches k), the element at the front of the deque is the maximum of the current subarray.</li></ul>Move the Window:<ul><li>Slide the window to the right by one position, and repeat the process for the next window of size k.</li></ul><h3>Example Walkthrough:</h3>Let’s walk through the example arr = [1, 3, -1, -3, 5, 3, 6, 7] with k = 3:Initial Subarray [1, 3, -1]:<ul><li>Max = 3 (indices stored in deque: [1])</li></ul>Next Subarray [3, -1, -3]:<ul><li>Max = 3 (deque maintains [1] after adjusting for window size)</li></ul>Next Subarray [-1, -3, 5]:<ul><li>Max = 5 (deque is updated to store [4] as 5 is greater than the previous elements)</li></ul>Next Subarray [-3, 5, 3]:<ul><li>Max = 5 (deque still maintains index [4])</li></ul>Next Subarray [5, 3, 6]:<ul><li>Max = 6 (deque updated to [6] after comparing and removing smaller elements)</li></ul>Next Subarray [3, 6, 7]:<ul><li>Max = 7 (deque updated to [7])</li></ul>The result is [3, 3, 5, 5, 6, 7].<h3>Time Complexity:</h3>The optimized solution using a deque has a time complexity of O(n)O(n)O(n), where n is the size of the array. This is because each element is added to and removed from the deque at most once. This solution is much more efficient than the brute-force approach.<h3>Applications of Sliding Window Maximum:</h3>Time Series Data Analysis:<ul><li>Sliding window maximum is useful for analyzing trends in time series data, where the goal is to track the maximum values over a specific window of time.</li></ul>Signal Processing:<ul><li>In signal processing, the sliding window maximum helps filter noise by keeping track of the highest signal values over a specific interval.</li></ul>Stock Market Analysis:<ul><li>For analyzing stock prices, sliding window maximum is used to compute the maximum stock price over a given period, such as a week or a month.</li></ul><h2>Why Learn Sliding Window Maximum?</h2>Understanding the sliding window maximum algorithm is crucial for solving problems that require efficient handling of subarrays or subsequences. Mastering this technique is useful for optimizing solutions in competitive programming, data analysis, and real-time processing of large datasets.Topics Included:Brute-Force vs. Optimized Approach: Comparison of brute-force and deque-based solutions for finding maximum values in sliding windows.Efficient Use of Deque: Step-by-step explanation of how the deque is used to maintain the maximum element in the current window.Real-World Applications: Practical scenarios where the sliding window maximum technique can be applied, such as time series analysis and stock market trends.For a detailed guide and further explanations, check out the full article on GeeksforGeeks: <a href="https://www.geeksforgeeks.org/sliding-window-maximum-maximum-of-all-subarrays-of-size-k/">https://www.geeksforgeeks.org/sliding-window-maximum-maximum-of-all-subarrays-of-size-k/</a>.

Sliding Window Maximum (Maximum of all subarrays of size K)

Given a queue, write a recursive function to reverse it.&nbsp; Standard operations allowed :&nbsp; enqueue(x) : Add an item x to rear of queue.&nbsp; dequeue() : Remove an item from front of queue.&nbsp; empty() : Checks if a queue is empty or not. Examples :&nbsp; &nbsp;Input : Q = [5, 24, 9, 6, 8, 4, 1, 8, 3, 6] Output : Q = [6, 3, 8, 1, 4, 8, 6, 9, 24, 5]&nbsp;Reversing a queue using recursion : <a href="https://www.geeksforgeeks.org/reversing-queue-using-recursion/">https://www.geeksforgeeks.org/reversing-queue-using-recursion/</a>

Reversing a queue using recursion

Welcome to the daily solving of our PROBLEM OF THE DAY with Nitin Kaplas We will discuss the entire problem step-by-step and work towards developing an optimized solution. This will not only help you brush up on your concepts of Graph but also build up problem-solving skills. You are given an Undirected Graph having unit weight of the edges, find the shortest path from src to all the vertex and if it is unreachable to reach any vertex, then return -1 for that vertex.Examples :Input:
n = 9, m = 10
edges=[[0,1],[0,3],[3,4],[4,5],[5,6],[1,2],[2,6],[6,7],[7,8],[6,8]] 
src=0
Output:
0 1 2 1 2 3 3 4 4Give the problem a try before going through the video. All the best!!! Problem Link: <a href="https://practice.geeksforgeeks.org/problems/shortest-path-in-undirected-graph-having-unit-distance/1">https://practice.geeksforgeeks.org/problems/shortest-path-in-undirected-graph-having-unit-distance/1</a>

PROBLEM OF THE DAY : 21/08/2024 | Shortest Path in Undirected Graph

This video is part of the Stack and Queue section under GFG SDE Sheet. Design a data structure that works like a LRU Cache. Here cap&nbsp;denotes&nbsp;the capacity of the cache and Q denotes the number of queries. Query can be of two types:<ol><li>SET x y: sets the value of the key x with value y</li><li>GET x: gets the key of x if present else returns -1.</li></ol> The LRUCache class has two methods get() and set() which are defined as follows.<ol><li>get(key): returns the value of the key if it&nbsp;already exists in the cache otherwise returns -1.</li><li>set(key, value): if the key is already present, update its value. If not present, add the key-value pair to the cache.&nbsp;If the cache reaches its capacity it should invalidate&nbsp;the least recently used item before inserting the new item.</li><li>In the constructor of the class the capacity of the cache should be initialized.</li></ol>&nbsp;Example 1:Input:
cap = 2
Q = 2
Queries = SET 1 2 GET 1
Output: 2
Explanation: 
Cache Size = 2

SET 1 2 GET 1
SET 1 2 : 1 -&gt; 2

GET 1 : Print the value corresponding
to Key 1, ie 2.Do check out:- Problem: <a href="https://www.geeksforgeeks.org/problems/lru-cache/1">https://www.geeksforgeeks.org/problems/lru-cache/1</a> SDE Sheet Link: <a href="https://www.geeksforgeeks.org/sde-sheet-a-complete-guide-for-sde-preparation/">https://www.geeksforgeeks.org/sde-sheet-a-complete-guide-for-sde-preparation/</a> Article Link:<a href=" https://www.geeksforgeeks.org/lru-cache-implementation/"> https://www.geeksforgeeks.org/lru-cache-implementation/</a>

SDE Sheet - LRU Cache