What is DSA?

Chapter 15: Searching Algorithms Introduction Searching and sorting algorithms are crucial in Data Structures and Algorithms (DSA) because they help us organize and access data efficiently. Whether you’re building a search engine, a database, or even a simple contact list, you’ll need to understand these algorithms deeply. This chapter focuses on two important tasks: Searching: Finding a specific element within a dataset. Sorting: Arranging data in a certain order (ascending, descending, etc.) for better management. To keep things interesting, we’ll break the chapter into digestible sections. First, we’ll focus on searching algorithms, and then in subsequent parts, we’ll cover sorting algorithms. As we proceed, remember to try the code examples on your IDE or notebook—this practice will cement your understanding. Part 1: Searching Algorithms 1.1 Linear Search Let’s start with one of the simplest searching algorithms: Linear Search. Though not the most efficient for large datasets, linear search is straightforward and can be used on unsorted data. Definition: Linear search traverses the entire data structure from the beginning to the end, comparing each element with the target value. If the target is found, the algorithm returns its position; otherwise, it returns a "not found" result. Use Case: Imagine you have a list of unsorted contact names. If you’re looking for someone, linear search is the only choice unless the list is sorted. Linear search is often used in small datasets where performance isn’t a concern. Algorithm: Here’s a simple breakdown of how linear search works: Start at the first element of the data structure. Compare the current element with the target value. If they match, return the index (position). If not, move to the next element. Repeat this process until you find the target or reach the end. Time Complexity: O(n) — because in the worst case, you might have to check every element. Let’s implement this in C and C++! C Programming: Linear Search in an Array #include <stdio.h> int linearSearch(int arr[], int n, int target) { for (int i = 0; i < n; i++) { if (arr[i] == target) { return i; // Target found, return the index } } return -1; // Target not found } int main() { int arr[] = {10, 23, 45, 70, 11, 15}; int n = sizeof(arr) / sizeof(arr[0]); int target = 70; int result = linearSearch(arr, n, target); if (result != -1) { printf("Element found at index: %d\n", result); } else { printf("Element not found.\n"); } return 0; } C++ Programming: Linear Search in an Array #include <iostream> using namespace std; int linearSearch(int arr[], int n, int target) { for (int i = 0; i < n; i++) { if (arr[i] == target) { return i; // Target found, return the index } } return -1; // Target not found } int main() { int arr[] = {2, 4, 0, 1, 9}; int n = sizeof(arr) / sizeof(arr[0]); int target = 1; int result = linearSearch(arr, n, target); if (result != -1) { cout << "Element found at index: " << result << endl; } else { cout << "Element not found." << endl; } return 0; } Explanation: In both programs, we create an array of integers and then implement the linear search function. The function traverses through the array, comparing each element with the target. If it finds the target, it returns the index of the element. If not, it returns -1, indicating that the element was not found. Discussion on Linked Lists: Linear search works similarly in Linked Lists, but instead of indexing like we do in arrays, we traverse the nodes. Since a linked list is a sequential structure without random access, we need to move from node to node. C Programming: Linear Search in a Singly Linked List #include <stdio.h> #include <stdlib.h> struct Node { int data; struct Node* next; }; int linearSearch(struct Node* head, int target) { struct Node* current = head; int index = 0; while (current != NULL) { if (current->data == target) { return index; } current = current->next; index++; } return -1; // Target not found } // Helper function to create a new node struct Node* createNode(int data) { struct Node* newNode = (struct Node*)malloc(sizeof(struct Node)); newNode->data = data; newNode->next = NULL; return newNode; } int main() { struct Node* head = createNode(10); head->next = createNode(20); head->next->next = createNode(30); head->next->next->next = createNode(40); int target = 30; int result = linearSearch(head, target); if (result != -1) { printf("Element found at position: %d\n", result); } else { printf("Element not found.\n"); } return 0; } Explanation: Here, we create a singly linked list and use a linear search algorithm to find the target value. We traverse the list node by node, checking each node’s data. Since there’s no concept of index in a linked list, we maintain a manual counter (index). If we find the target, we return its position. Otherwise, we return -1. Advantages and Disadvantages of Linear Search: Advantages: Simple and easy to implement. Works on both sorted and unsorted datasets. Disadvantages: Inefficient for large datasets, especially when compared to more advanced algorithms like binary search. Takes linear time in the worst case (O(n)). Where to Use Linear Search: When dealing with small datasets or unsorted data. For data structures like linked lists, where other search algorithms might be less efficient due to lack of direct indexing. This concludes our detailed discussion of Linear Search across different data structures. Next Part Preview: Binary Search In the next part of the chapter, we will dive into the more efficient Binary Search, where we optimize the searching process by dividing the dataset in half. But remember, binary search only works on sorted datasets, so sorting comes into play! You’ll also see how binary search works not just with arrays but with Binary Search Trees (BSTs), a crucial data structure in DSA. And as always, feel free to check out resources on digilearn.cloud from Emancipation Edutech Private Limited, where this book is available for free reading! Part 2: Binary Search 1.2 Binary

Chapter 14: Tree Traversal Methods In this chapter, we will explore Tree Traversal Methods, which are vital for accessing and processing the data stored in tree structures. Trees are non-linear data structures that represent hierarchical relationships, making traversing them crucial for various applications, such as searching, sorting, and modifying data. Traversing a tree means visiting all its nodes in a specified order. The choice of traversal method can significantly impact the efficiency and outcome of operations performed on the tree. There are two main categories of tree traversal methods: Depth-First Traversal (DFT) Breadth-First Traversal (BFT) 1. Depth-First Traversal (DFT) Depth-First Traversal explores as far down one branch of the tree as possible before backtracking to explore other branches. This method is typically implemented using recursion or an explicit stack data structure. There are three primary types of Depth-First Traversal: 1.1 Pre-Order Traversal In Pre-Order Traversal, nodes are visited in the following order: Visit the current node. Traverse the left subtree. Traverse the right subtree. Use Cases for Pre-Order Traversal Copying a Tree: If you need to create a duplicate of a tree, pre-order traversal allows you to visit each node, storing its value in a new tree structure. Prefix Expression Evaluation: In expressions written in prefix notation (also known as Polish notation), pre-order traversal is essential for evaluating the expression. Example: Given the binary tree: A / \ B C / \ D E The Pre-Order Traversal would yield: A, B, D, E, C. Implementation: class Node: def __init__(self, key): self.left = None self.right = None self.val = key def pre_order_traversal(root): if root: print(root.val, end=’ ‘) # Visit the node pre_order_traversal(root.left) # Traverse left subtree pre_order_traversal(root.right) # Traverse right subtree # Create the tree root = Node(‘A’) root.left = Node(‘B’) root.right = Node(‘C’) root.left.left = Node(‘D’) root.left.right = Node(‘E’) print("Pre-Order Traversal:") pre_order_traversal(root) # Output: A B D E C 1.2 In-Order Traversal In In-Order Traversal, nodes are visited in this order: Traverse the left subtree. Visit the current node. Traverse the right subtree. Use Cases for In-Order Traversal Binary Search Trees (BST): In-order traversal is used to retrieve the elements of a BST in sorted order. Expression Tree Evaluation: For expression trees, in-order traversal can be useful for generating infix notation. Example: Using the same binary tree, the In-Order Traversal yields: D, B, E, A, C. Implementation: def in_order_traversal(root): if root: in_order_traversal(root.left) # Traverse left subtree print(root.val, end=’ ‘) # Visit the node in_order_traversal(root.right) # Traverse right subtree print("\nIn-Order Traversal:") in_order_traversal(root) # Output: D B E A C 1.3 Post-Order Traversal In Post-Order Traversal, nodes are visited in this order: Traverse the left subtree. Traverse the right subtree. Visit the current node. Use Cases for Post-Order Traversal Deleting a Tree: When deallocating memory for tree nodes, post-order traversal ensures that child nodes are deleted before the parent node. Postfix Expression Evaluation: In postfix notation (Reverse Polish notation), post-order traversal is used for evaluation. Example: Again, using the same binary tree, the Post-Order Traversal yields: D, E, B, C, A. Implementation: def post_order_traversal(root): if root: post_order_traversal(root.left) # Traverse left subtree post_order_traversal(root.right) # Traverse right subtree print(root.val, end=’ ‘) # Visit the node print("\nPost-Order Traversal:") post_order_traversal(root) # Output: D E B C A 2. Breadth-First Traversal (BFT) Breadth-First Traversal explores all the nodes at the present depth level before moving on to the nodes at the next depth level. This traversal method is typically implemented using a queue data structure. 2.1 Level Order Traversal In Level Order Traversal, the nodes are visited level by level from top to bottom. Starting from the root, it visits all nodes at the present depth level before moving on to the nodes at the next depth level. Use Cases for Level Order Traversal Finding the Shortest Path: In unweighted trees or graphs, level order traversal is useful for finding the shortest path between nodes. Completeness Checking: To check whether a binary tree is complete, level order traversal can help validate the condition at each level. Example: Using the same binary tree, the Level Order Traversal yields: A, B, C, D, E. Implementation: from collections import deque def level_order_traversal(root): if root is None: return queue = deque([root]) # Initialize the queue with the root node while queue: current = queue.popleft() # Dequeue the front node print(current.val, end=’ ‘) # Visit the node if current.left: # Enqueue left child queue.append(current.left) if current.right: # Enqueue right child queue.append(current.right) print("\nLevel Order Traversal:") level_order_traversal(root) # Output: A B C D E Comparison of Traversal Methods Traversal Method Order of Visiting Nodes Use Cases Pre-Order Node, Left, Right Copying a tree, prefix expression In-Order Left, Node, Right Binary search trees, sorted output Post-Order Left, Right, Node Deleting a tree, postfix expression Level Order Level by Level Finding shortest path in unweighted trees Complexity Analysis Understanding the time and space complexity of these traversal methods is essential: Time Complexity: All traversal methods (pre-order, in-order, post-order, and level order) have a time complexity of O(n), where n is the number of nodes in the tree. Each node is visited exactly once. Space Complexity: Pre-Order, In-Order, and Post-Order: The space complexity for these recursive methods is O(h), where h is the height of the tree. This is due to the stack space used by recursive calls. For balanced trees, this is O(log n), but for skewed trees, it can be O(n). Level Order: The space complexity for level order traversal is O(w), where w is the maximum width of the tree. In the worst case, this can also be O(n). Conclusion Tree traversal methods are fundamental for effectively working with tree data structures. They allow us to explore and manipulate tree nodes efficiently, which is essential for various applications in computer science and programming. In our next chapter, we will delve into Binary Trees and Binary Search Trees (BST), laying the groundwork for understanding more complex tree structures. Remember, for free access to this book and other resources provided by Emancipation Edutech Private Limited, you can visit digilearn.cloud. Happy learning!

Chapter 13: AVL Trees and Red-Black Trees In this chapter, we will dive into AVL Trees and Red-Black Trees, two important types of self-balancing binary search trees. These trees ensure that the height of the tree remains balanced, providing improved efficiency for operations such as insertion, deletion, and searching. Self-balancing trees like AVL Trees and Red-Black Trees guarantee that the operations on the tree have a time complexity of O(log n), even in the worst-case scenario. Let’s explore them in detail! What are AVL Trees? An AVL Tree is a type of self-balancing binary search tree where the difference between the heights of the left and right subtrees of any node (known as the balance factor) is at most 1. The AVL Tree is named after its inventors Adelson-Velsky and Landis, who introduced the concept in 1962. Key Properties of AVL Trees: Balance Factor: For every node in an AVL tree, the balance factor must be either -1, 0, or +1. Balance Factor = Height of Left Subtree – Height of Right Subtree If the balance factor is not within this range, the tree is considered unbalanced and requires rotation to rebalance. Rotations: To maintain the balance of an AVL tree, we use rotations when an insertion or deletion operation causes the tree to become unbalanced. Single Rotations: Right or Left rotation. Double Rotations: A combination of right and left rotations. Example of an AVL Tree Consider inserting the elements 10, 20, and 30 into an empty AVL Tree. Step 1: Insert 10: 10 Step 2: Insert 20: 10 \ 20 Step 3: Insert 30. This causes the tree to become unbalanced because the balance factor of node 10 becomes -2 (left height = 0, right height = 2). To rebalance the tree, we perform a left rotation at node 10: Before rotation: 10 \ 20 \ 30 After rotation: 20 / \ 10 30 Now, the tree is balanced again. Rotations in AVL Trees To maintain the balance factor of AVL trees, we perform rotations. There are four types of rotations used to rebalance an AVL tree: 1. Left Rotation (LL Rotation) A left rotation is performed when a node becomes unbalanced due to the right subtree being taller than the left subtree. This happens when a new node is inserted into the right subtree of the right child. Example: Before left rotation: 10 \ 20 \ 30 After left rotation: 20 / \ 10 30 2. Right Rotation (RR Rotation) A right rotation is performed when a node becomes unbalanced due to the left subtree being taller than the right subtree. This happens when a new node is inserted into the left subtree of the left child. Example: Before right rotation: 30 / 20 / 10 After right rotation: 20 / \ 10 30 3. Left-Right Rotation (LR Rotation) A left-right rotation is a combination of left and right rotations. It is performed when a new node is inserted into the right subtree of the left child. Example: Before LR rotation: 30 / 10 \ 20 First, perform a left rotation on the left child (10), then perform a right rotation on the root (30): After LR rotation: 20 / \ 10 30 4. Right-Left Rotation (RL Rotation) A right-left rotation is a combination of right and left rotations. It is performed when a new node is inserted into the left subtree of the right child. Example: Before RL rotation: 10 \ 30 / 20 First, perform a right rotation on the right child (30), then perform a left rotation on the root (10): After RL rotation: 20 / \ 10 30 What are Red-Black Trees? A Red-Black Tree is another type of self-balancing binary search tree. It is similar to an AVL tree, but it has a more relaxed balancing criterion, which makes it faster for insertion and deletion operations. The key feature of a Red-Black Tree is the use of colors to maintain balance. Key Properties of Red-Black Trees: Each node is colored either red or black. The root node must always be black. No two consecutive red nodes can appear on the same path (i.e., a red node cannot have a red parent or red child). Every path from a node to its descendant null nodes must contain the same number of black nodes. The longest path from the root to a leaf is no more than twice as long as the shortest path (this ensures that the tree is balanced). Example of a Red-Black Tree Consider inserting the elements 10, 20, and 30 into an empty Red-Black Tree. Step 1: Insert 10 (the root node is always black). 10 (black) Step 2: Insert 20. Since it is greater than 10, it is inserted as the right child and colored red: 10 (black) \ 20 (red) Step 3: Insert 30. Since this creates a violation of two consecutive red nodes (20 and 30), we perform a left rotation at node 10 and recolor: Before rotation: 10 (black) \ 20 (red) \ 30 (red) After rotation and recoloring: 20 (black) / \ 10 (red) 30 (red) Now, the tree satisfies all the Red-Black Tree properties. Rotations in Red-Black Trees Similar to AVL trees, Red-Black Trees also use rotations to restore balance when a violation occurs. The rotations are the same as those in AVL trees: left rotation, right rotation, left-right rotation, and right-left rotation. Comparison: AVL Trees vs. Red-Black Trees Feature AVL Trees Red-Black Trees Balancing Method Strictly balanced Loosely balanced Height Difference At most 1 No more than twice the shortest path Rebalancing Frequent rotations Fewer rotations Efficiency in Insertion/Deletion Slower in large trees Faster for insertion/deletion Search Efficiency Slightly better Slightly worse than AVL trees Applications of AVL and Red-Black Trees Database Indexing: Both AVL and Red-Black Trees are widely used in databases for maintaining ordered records efficiently. Memory Management: Red-Black Trees are often used in memory allocators (e.g., Linux kernel uses Red-Black Trees). Networking: AVL and Red-Black Trees are useful in routing

Chapter 12: Binary Trees and Binary Search Trees (BST) Welcome to the fascinating world of Trees! Trees are one of the most fundamental data structures in computer science, and they are widely used in various applications like file systems, databases, and more. This chapter focuses on two essential types of trees: Binary Trees and Binary Search Trees (BSTs). What is a Tree? A Tree is a non-linear data structure composed of nodes. It is a hierarchical structure with a root node and sub-nodes, each connected by edges. Unlike arrays, linked lists, stacks, or queues, trees are nonlinear and allow for more complex relationships between elements. A tree follows these rules: Each node contains a value or data. The root node is the topmost node in the tree. Each node has zero or more child nodes. Each node can only have one parent (except for the root, which has no parent). What is a Binary Tree? A Binary Tree is a type of tree where each node can have at most two children, often referred to as the left child and right child. Characteristics of a Binary Tree: Node: Contains data, and two pointers (left and right). Edge: The connection between two nodes. Root: The topmost node in the tree. Leaf Node: A node with no children. Height: The number of edges from the root to the deepest leaf. Structure of a Binary Tree Here’s a simple binary tree: 10 / \ 5 15 / \ / \ 3 7 12 20 In this example: 10 is the root. 5 and 15 are children of 10. 3, 7, 12, and 20 are leaf nodes. Types of Binary Trees Full Binary Tree: Every node has either 0 or 2 children. Complete Binary Tree: All levels are completely filled, except possibly the last level, which is filled from left to right. Perfect Binary Tree: All internal nodes have two children, and all leaves are at the same level. Balanced Binary Tree: The height of the tree is balanced, meaning the difference between the height of the left and right subtrees for any node is at most 1. What is a Binary Search Tree (BST)? A Binary Search Tree (BST) is a special kind of binary tree where the nodes are arranged in a specific order. In a BST: The left subtree of a node contains only nodes with values less than the node’s value. The right subtree contains only nodes with values greater than the node’s value. Both the left and right subtrees must also be binary search trees. This property makes BSTs highly efficient for searching, insertion, and deletion operations. Example of a Binary Search Tree Consider the following tree: 15 / \ 10 25 / \ / \ 5 12 20 30 In this BST: Nodes to the left of 15 are smaller than 15 (i.e., 10, 5, 12). Nodes to the right of 15 are greater than 15 (i.e., 25, 20, 30). This ordering ensures efficient search operations. Operations on Binary Search Trees 1. Insertion To insert a new element into a BST, we start at the root and compare the new element with the current node: If it is smaller, we move to the left subtree. If it is greater, we move to the right subtree. We continue this until we find an empty spot, where we insert the new node. Example: Inserting 8 into the following BST: 10 / \ 5 15 / \ 3 7 Since 8 is greater than 5 and less than 10, we move to the right child of 5 and place it there: 10 / \ 5 15 / \ 3 7 \ 8 2. Deletion To delete a node from a BST, we must consider three cases: Case 1: The node to be deleted is a leaf (has no children). Case 2: The node to be deleted has one child. Case 3: The node to be deleted has two children. Example: Let’s delete the node 7 from the tree above. Since it has one child (8), we simply replace 7 with 8. 10 / \ 5 15 / \ 3 8 3. Search To search for a value in a BST: Start at the root. If the value is smaller than the current node, move left; if it’s greater, move right. Repeat this process until you find the value or reach a NULL node (the value does not exist in the tree). Applications of Binary Search Trees Search Operations: Due to the structured order, searching for an element in a BST is more efficient than in unsorted arrays or linked lists. In-memory databases: BSTs are commonly used in database indexing where efficient searching is crucial. Network Routing: BSTs help in storing hierarchical data, which can be efficiently searched, like network routes. Dynamic Data Structures: In scenarios like building self-balancing trees in applications, BSTs form the backbone. Systems like Emancipation Edutech Private Limited often use Binary Search Trees to manage student records efficiently by indexing them according to registration numbers. For free educational resources, visit digilearn.cloud, where you can access this book and much more! Binary Tree Traversal Methods To interact with the data in a binary tree, we need to traverse it. There are three primary traversal techniques: 1. In-order Traversal (Left, Root, Right) This traversal visits nodes in ascending order in a BST. Visit Left Subtree -> Visit Root -> Visit Right Subtree Example: For the tree: 10 / \ 5 15 / \ 3 7 The in-order traversal would be: 3 -> 5 -> 7 -> 10 -> 15. 2. Pre-order Traversal (Root, Left, Right) This traversal visits the root node first, followed by the left subtree, and then the right subtree. Visit Root -> Visit Left Subtree -> Visit Right Subtree Example: For the same tree: 10 / \ 5 15 / \ 3 7 The pre-order traversal would be: 10 -> 5 -> 3 -> 7 -> 15. 3. Post-order Traversal (Left, Right, Root) This traversal visits

Chapter 11: Deques (Double-Ended Queues) Welcome to Chapter 11! In this chapter, we’ll explore Deques, or Double-Ended Queues, an interesting and versatile data structure that extends the concept of a standard queue by allowing insertion and deletion at both ends. This concept will help you understand more advanced applications of queues and solve real-life problems like managing text editors, undo operations, or simulations. Let’s begin! What is a Deque? A Deque (pronounced “deck”) stands for Double-Ended Queue, and it is a generalized form of the standard queue. Unlike a regular queue, where insertion is done at the back and deletion is done at the front, a deque allows insertion and deletion from both ends of the structure. In simple terms: You can add an element at either the front or the back. You can remove an element from either the front or the back. This makes the deque a very flexible data structure. Imagine you’re standing in a line (queue) but now, you have the option to add someone to the front of the line, or let them sneak in at the back. That’s how a deque works! Types of Deques Deques can be classified into two types: Input-Restricted Deque: In this type of deque, insertion is restricted to one end (say, the rear), but deletion can occur at both ends. Output-Restricted Deque: In this type, deletion is restricted to one end (say, the front), but insertion can happen at both ends. Both types allow different levels of control depending on your use case. Applications of Deques Before we get into the operations, let’s explore some real-world applications of deques: Undo/Redo Operations: In text editors or drawing tools, deques can be used to manage undo and redo functionality. You can easily move forward and backward in the history of actions. Palindrome Checking: Deques are perfect for checking palindromes (words or phrases that read the same forward and backward) because they allow access from both ends. Sliding Window Problems: In algorithms, deques are often used to maintain a sliding window of data, useful in problems like finding the maximum or minimum of all subarrays of a given size. Task Scheduling: In simulations or real-time systems like the ones developed by Emancipation Edutech Private Limited, deques can manage tasks with both high and low priorities, offering flexibility in choosing which task to execute next. You can read more about these applications and other use cases for free on digilearn.cloud! Basic Operations of Deques Now, let’s break down the core operations that can be performed on a deque: Insertion at Front: Insert an element at the front of the deque. Insertion at Rear: Insert an element at the rear of the deque. Deletion from Front: Remove an element from the front of the deque. Deletion from Rear: Remove an element from the rear of the deque. Get Front: Retrieve the front element without removing it. Get Rear: Retrieve the rear element without removing it. Check if Empty: Check if the deque is empty. Check if Full: Check if the deque is full (in a limited-size implementation). These operations make deques highly versatile, with various possibilities for manipulating the data structure. Deque Representation and Implementation Let’s implement a deque using an array in C to better understand how it works. Define the Deque Structure #include <stdio.h> #include <stdlib.h> #define MAX 100 struct Deque { int arr[MAX]; // Array to store elements int front; // Points to the front end int rear; // Points to the rear end int size; // Current number of elements }; arr: This array will store our elements. front: This pointer will indicate the front of the deque. rear: This pointer will indicate the rear of the deque. size: This will track the number of elements in the deque. Initialize the Deque void initialize(struct Deque* dq) { dq->front = -1; // Initially, front is -1 (empty state) dq->rear = -1; // Initially, rear is -1 (empty state) dq->size = 0; // No elements in the deque } Insertion at Front void insertFront(struct Deque* dq, int value) { if (dq->size == MAX) { // Check if deque is full printf("Deque Overflow\n"); return; } if (dq->front == -1) { // First element insertion dq->front = 0; dq->rear = 0; } else if (dq->front == 0) { // Circular condition dq->front = MAX – 1; } else { dq->front–; } dq->arr[dq->front] = value; dq->size++; } Insertion at Rear void insertRear(struct Deque* dq, int value) { if (dq->size == MAX) { // Check if deque is full printf("Deque Overflow\n"); return; } if (dq->rear == -1) { // First element insertion dq->rear = 0; dq->front = 0; } else if (dq->rear == MAX – 1) { // Circular condition dq->rear = 0; } else { dq->rear++; } dq->arr[dq->rear] = value; dq->size++; } Deletion from Front void deleteFront(struct Deque* dq) { if (dq->size == 0) { // Check if deque is empty printf("Deque Underflow\n"); return; } if (dq->front == dq->rear) { // Only one element in deque dq->front = -1; dq->rear = -1; } else if (dq->front == MAX – 1) { // Circular condition dq->front = 0; } else { dq->front++; } dq->size–; } Deletion from Rear void deleteRear(struct Deque* dq) { if (dq->size == 0) { // Check if deque is empty printf("Deque Underflow\n"); return; } if (dq->front == dq->rear) { // Only one element in deque dq->front = -1; dq->rear = -1; } else if (dq->rear == 0) { // Circular condition dq->rear = MAX – 1; } else { dq->rear–; } dq->size–; } Checking if the Deque is Empty or Full int isEmpty(struct Deque* dq) { return dq->size == 0; } int isFull(struct Deque* dq) { return dq->size == MAX; } Real-World Example Imagine you’re developing a browser. The deque can be used to implement forward and backward navigation: You can insert URLs into the deque as the user browses different websites. When the user clicks the "back" button, you remove the URL from the rear (current page) and go back

Chapter 10: Priority Queues Welcome to Chapter 10! In this chapter, we’ll explore Priority Queues, a special type of queue where elements are processed based on their priority rather than the order in which they were added. Priority queues are incredibly useful in various real-world applications, such as task scheduling, Dijkstra’s shortest path algorithm, and handling network packets. Let’s dive into the concept and understand how they work! What is a Priority Queue? A Priority Queue is a data structure where each element is associated with a priority, and the element with the highest (or lowest) priority is dequeued first. Unlike a regular queue, which follows the First-In-First-Out (FIFO) principle, priority queues can rearrange the order of processing based on priorities. For example, in an emergency room, patients with life-threatening conditions (higher priority) will be treated first, even if they arrived after other patients with minor injuries. Here’s a basic visual of how a priority queue works: Priority Queue: [Critical Patient (Priority 3)] -> [Moderate Patient (Priority 2)] -> [Minor Patient (Priority 1)] In this case, the Critical Patient will be dequeued and treated first, regardless of the order in which they entered the queue. Types of Priority Queues There are two types of priority queues: Max-Priority Queue: In this type, the element with the highest priority is dequeued first. Min-Priority Queue: In this type, the element with the lowest priority is dequeued first. For instance, in task scheduling systems at Emancipation Edutech Private Limited, a max-priority queue might be used to prioritize the most urgent student requests first. Priority Queue Operations The following operations are performed on priority queues: Insert (Enqueue): Add an element to the queue with a given priority. Extract Max (Dequeue): Remove the element with the highest priority (in max-priority queues). Get Max: Retrieve, but do not remove, the element with the highest priority. Increase Priority: Change the priority of an element in the queue. Let’s understand these operations more deeply by implementing a priority queue using arrays. Priority Queue Implementation Using Arrays To keep things simple, we’ll implement a max-priority queue using arrays, where higher numbers indicate higher priorities. Define the Queue Structure #include <stdio.h> #include <stdlib.h> #define MAX 100 // Max size of the priority queue struct PriorityQueue { int data[MAX]; // Array to store elements int priority[MAX]; // Array to store priorities int size; // Number of elements in the queue }; data: Stores the elements. priority: Stores the priority associated with each element. size: Tracks the current number of elements in the queue. Initialize the Priority Queue void initialize(struct PriorityQueue* pq) { pq->size = 0; // Queue is initially empty } Insert Operation void insert(struct PriorityQueue* pq, int value, int prio) { if (pq->size == MAX) { // Check if the queue is full printf("Queue Overflow\n"); return; } int i = pq->size; pq->data[i] = value; pq->priority[i] = prio; pq->size++; } Here, we simply insert an element along with its priority at the end of the queue. Extract Max Operation int extractMax(struct PriorityQueue* pq) { if (pq->size == 0) { // Check if the queue is empty printf("Queue Underflow\n"); return -1; } int maxIndex = 0; // Find the element with the highest priority for (int i = 1; i < pq->size; i++) { if (pq->priority[i] > pq->priority[maxIndex]) { maxIndex = i; } } int maxValue = pq->data[maxIndex]; // Store the highest priority value // Shift the remaining elements for (int i = maxIndex; i < pq->size – 1; i++) { pq->data[i] = pq->data[i + 1]; pq->priority[i] = pq->priority[i + 1]; } pq->size–; return maxValue; } In this operation, we scan the queue to find the element with the highest priority and then remove it, shifting the rest of the elements. Example Usage int main() { struct PriorityQueue pq; initialize(&pq); // Insert elements with priorities insert(&pq, 10, 1); // Insert value 10 with priority 1 insert(&pq, 20, 4); // Insert value 20 with priority 4 insert(&pq, 30, 3); // Insert value 30 with priority 3 // Extract the highest priority element printf("Extracted element: %d\n", extractMax(&pq)); return 0; } Output: Extracted element: 20 Notice how 20 was extracted because it had the highest priority (4). Using Priority Queues in Real Life Priority queues are extremely useful in real-world applications. Let’s explore some common scenarios: Task Scheduling: Operating systems use priority queues to manage tasks. Processes with higher priority are executed before lower-priority processes, ensuring time-sensitive tasks are handled quickly. Network Traffic: In network systems, priority queues help manage data packets. Critical packets (such as emergency notifications) are transmitted first, while less important packets (like email) are handled later. Dijkstra’s Algorithm: Priority queues are used in algorithms like Dijkstra’s Shortest Path Algorithm. Nodes with the smallest distance (priority) are processed first, helping to find the shortest path between two points efficiently. Emergency Systems: Priority queues manage patients in hospitals, where the most critical patients (with higher priorities) are treated first. At Emancipation Edutech Private Limited, we could use priority queues to manage student queries or schedule courses based on demand, ensuring that high-demand courses are given priority. Don’t forget to explore digilearn.cloud to access more free educational resources! Implementing Priority Queues Using Heaps While arrays are a simple way to implement priority queues, they’re not the most efficient. For faster access to the highest-priority element, heaps are commonly used. A binary heap allows both insertion and extraction of the maximum element in O(log n) time, compared to O(n) time with arrays. Let’s see how a max-heap works with priority queues: Max-Heap Representation A max-heap is a binary tree where the parent node is always greater than or equal to its children. This ensures that the root always contains the highest-priority element, which can be removed efficiently. 20 / \ 15 10 / \ 8 5 Here, 20 is the maximum value, and extracting it will require only O(log n) time to rearrange the heap. Applications of Priority Queues Priority queues are widely used in areas like: Operating Systems: For task scheduling and resource management.

Chapter 9: Circular Queues Welcome to Chapter 9! In this chapter, we’ll explore Circular Queues, which are an extension of the basic queue structure. Circular queues optimize memory usage by reusing spaces that are freed when elements are dequeued. This makes them highly useful for scenarios with fixed-size buffers, like operating systems and network packet handling. What is a Circular Queue? A Circular Queue is a type of queue in which the last position is connected back to the first position, making the queue circular. This means that when the rear of the queue reaches the end of the array, it wraps around to the front (if there’s space) to reuse the vacated positions. Circular queues solve the problem of wasted space in a standard queue implementation where elements are removed from the front but the space isn’t reused. Here’s a basic diagram of a circular queue: Front ↓ [5] -> [6] -> [7] -> [8] -> [0] ↑ Rear In this circular queue, after adding an element to the rear, we’ll wrap around to the front to fill any vacated positions. Why Use a Circular Queue? In a normal queue, once the rear pointer reaches the end of the array, no more elements can be added, even if there are free spaces in the front due to dequeued elements. Circular queues resolve this by allowing the rear pointer to wrap around to the front and reuse those free spaces. At Emancipation Edutech Private Limited, a circular queue could be used in systems where students access learning modules. When students complete a module and move on, their slot in the queue can be reused by another student. Operations on Circular Queues The operations on circular queues are similar to those on regular queues but with a few modifications to handle the circular nature. The following are the main operations: Enqueue: Add an element to the rear of the circular queue. Dequeue: Remove an element from the front of the circular queue. Front: Get the front element of the circular queue. IsFull: Check if the queue is full. IsEmpty: Check if the queue is empty. Let’s see how to implement these operations using an array-based circular queue. Circular Queue Implementation Using an Array Define the Queue Structure #include <stdio.h> #include <stdlib.h> #define MAX 5 // Size of the circular queue struct CircularQueue { int front, rear; int arr[MAX]; }; front: Index of the front element. rear: Index of the rear element. arr: Array to store the elements. Initialize the Circular Queue void initialize(struct CircularQueue* q) { q->front = -1; q->rear = -1; // Queue is initially empty } Enqueue Operation void enqueue(struct CircularQueue* q, int value) { if ((q->rear + 1) % MAX == q->front) { // Check if the queue is full printf("Queue Overflow\n"); return; } if (q->front == -1) // If queue is initially empty q->front = 0; q->rear = (q->rear + 1) % MAX; // Increment rear in a circular manner q->arr[q->rear] = value; // Add the value } In this case, we use the modulo (%) operator to wrap around the rear pointer when it reaches the end of the array. Dequeue Operation int dequeue(struct CircularQueue* q) { if (q->front == -1) { // Check if the queue is empty printf("Queue Underflow\n"); return -1; // Return -1 to indicate an error } int value = q->arr[q->front]; // Store the front value if (q->front == q->rear) { // If there was only one element q->front = -1; q->rear = -1; // Reset the queue } else { q->front = (q->front + 1) % MAX; // Increment front in a circular manner } return value; } Front Operation int front(struct CircularQueue* q) { if (q->front == -1) { // Check if the queue is empty printf("Queue is Empty\n"); return -1; } return q->arr[q->front]; // Return the front value } Example Usage int main() { struct CircularQueue queue; initialize(&queue); // Enqueue elements enqueue(&queue, 10); enqueue(&queue, 20); enqueue(&queue, 30); enqueue(&queue, 40); // Dequeue an element printf("Dequeued element: %d\n", dequeue(&queue)); // Enqueue another element enqueue(&queue, 50); // Print the front element printf("Front element: %d\n", front(&queue)); return 0; } Output: Dequeued element: 10 Front element: 20 Notice how, after dequeueing 10, we enqueue another element 50, reusing the space previously held by 10. Circular Queue vs. Linear Queue Let’s compare circular queues and linear queues to understand the key differences: Feature Circular Queue Linear Queue Memory Utilization Utilizes memory efficiently by reusing vacated positions after dequeueing. Wastes memory as vacated positions aren’t reused. Overflow Condition Occurs only when all positions are filled, even if front has moved from the first index. Occurs when the rear reaches the end, even if there are empty spaces at the front. Example Usage Used in scheduling, buffering, and resource management systems like CPU scheduling or network packet handling. Used in simpler cases, like managing a single queue of tasks. Circular queues are perfect when you need to manage fixed-size buffers and prevent wasted space. At Emancipation Edutech Private Limited, circular queues might be used in our learning management systems to efficiently handle student queries or manage the flow of study resources—ensuring no slot goes to waste. To learn more, visit digilearn.cloud, where you can read this book and other educational resources for free. Applications of Circular Queues Circular queues are used in several real-world applications: CPU Scheduling: Circular queues are often used to manage processes in the round-robin CPU scheduling algorithm. In this method, each process is given an equal share of the CPU, and once its time slice is over, it goes back to the end of the queue. Network Buffers: Circular queues are used in network systems to handle incoming data packets. When one packet is processed, the slot is reused for a new incoming packet, preventing overflow until all slots are filled. Job Scheduling: Printers and other resource management systems use circular queues to manage multiple print jobs without wasting space. Real-Time Systems: In real-time computing environments, circular queues help in managing time-critical

Chapter 8: Queues Welcome to Chapter 8! In this chapter, we’ll explore Queues, another fundamental data structure that follows the First-In-First-Out (FIFO) principle. Queues are widely used in various computing scenarios like task scheduling, resource management, and buffering. What is a Queue? A Queue is a linear data structure where elements are inserted at the rear (also called the back) and removed from the front. This follows the First-In-First-Out (FIFO) principle, which means the first element added to the queue will be the first one to be removed, much like a line of people waiting at a ticket counter. Here’s a basic diagram of a queue: Front -> [1] -> [2] -> [3] -> [4] <- Rear In this queue, 1 is the front element and will be removed first, while 4 is at the rear and will be removed last. Why Use Queues? Queues are especially useful in situations where you need to manage tasks or resources in the order they arrive. They are widely applied in various domains: CPU Scheduling: Operating systems use queues to manage processes waiting for execution. Network Traffic Management: Queues are used to buffer incoming network packets before processing. Printer Job Management: When multiple print jobs are sent to a printer, they are queued and printed in the order they were received. At Emancipation Edutech Private Limited, queues may be used to manage student requests or service multiple learning queries simultaneously, ensuring that tasks are handled in the order they arrive. Basic Operations on Queues Queues support four essential operations: Enqueue: Add an element to the rear of the queue. Dequeue: Remove an element from the front of the queue. Front: Get the front element of the queue. IsEmpty: Check if the queue is empty. Let’s see how to implement these operations using both array-based and linked-list-based queues. Queue Implementation Using an Array An array-based queue is implemented using a fixed-size array to store the elements. However, managing the front and rear pointers becomes essential to keep track of the queue. Define the Queue Structure #include <stdio.h> #include <stdlib.h> #define MAX 100 // Maximum size of the queue // Define the queue structure struct Queue { int front, rear; int arr[MAX]; }; front: Index of the front element. rear: Index of the rear element. arr: Array to hold the elements. Initialize the Queue void initialize(struct Queue* q) { q->front = -1; q->rear = -1; // Queue is empty initially } Enqueue Operation void enqueue(struct Queue* q, int value) { if (q->rear == MAX – 1) { // Check for overflow printf("Queue Overflow\n"); return; } if (q->front == -1) // If queue is initially empty q->front = 0; q->arr[++(q->rear)] = value; // Increment rear and add value } Dequeue Operation int dequeue(struct Queue* q) { if (q->front == -1 || q->front > q->rear) { // Check for underflow printf("Queue Underflow\n"); return -1; // Return -1 to indicate an error } return q->arr[q->front++]; // Return front value and increment front } Front Operation int front(struct Queue* q) { if (q->front == -1 || q->front > q->rear) { // Check if queue is empty printf("Queue is Empty\n"); return -1; } return q->arr[q->front]; // Return front value } Example Usage int main() { struct Queue queue; initialize(&queue); // Enqueue elements enqueue(&queue, 10); enqueue(&queue, 20); enqueue(&queue, 30); // Get the front element printf("Front element is %d\n", front(&queue)); // Dequeue elements printf("Dequeued element is %d\n", dequeue(&queue)); printf("Dequeued element is %d\n", dequeue(&queue)); return 0; } Output: Front element is 10 Dequeued element is 10 Dequeued element is 20 Queue Implementation Using a Linked List In a linked-list-based queue, we dynamically allocate memory for each element, allowing the queue to grow as needed without a fixed size limit. Define the Node Structure #include <stdio.h> #include <stdlib.h> // Define the node structure struct Node { int data; struct Node* next; }; // Define the queue structure struct Queue { struct Node* front; struct Node* rear; }; Initialize the Queue void initialize(struct Queue* q) { q->front = q->rear = NULL; // Queue is empty initially } Enqueue Operation void enqueue(struct Queue* q, int value) { struct Node* newNode = (struct Node*)malloc(sizeof(struct Node)); newNode->data = value; newNode->next = NULL; if (q->rear == NULL) { // If queue is empty q->front = q->rear = newNode; return; } q->rear->next = newNode; q->rear = newNode; } Dequeue Operation int dequeue(struct Queue* q) { if (q->front == NULL) { // Check if queue is empty printf("Queue Underflow\n"); return -1; // Return -1 to indicate an error } struct Node* temp = q->front; int value = temp->data; q->front = q->front->next; if (q->front == NULL) { // If queue becomes empty q->rear = NULL; } free(temp); return value; } Front Operation int front(struct Queue* q) { if (q->front == NULL) { // Check if queue is empty printf("Queue is Empty\n"); return -1; } return q->front->data; // Return front value } Example Usage int main() { struct Queue queue; initialize(&queue); // Enqueue elements enqueue(&queue, 10); enqueue(&queue, 20); enqueue(&queue, 30); // Get the front element printf("Front element is %d\n", front(&queue)); // Dequeue elements printf("Dequeued element is %d\n", dequeue(&queue)); printf("Dequeued element is %d\n", dequeue(&queue)); return 0; } Output: Front element is 10 Dequeued element is 10 Dequeued element is 20 Types of Queues There are several variations of the basic queue that serve different purposes in computer science: 1. Simple Queue: The basic queue structure we have seen so far. Elements are inserted at the rear and removed from the front. 2. Circular Queue: In a circular queue, the rear of the queue wraps around to the front when the end of the array is reached. This allows for efficient use of space by reusing positions that are freed when elements are dequeued. if (rear == MAX – 1) { rear = 0; } Circular queues are often used in resource management systems like printer spoolers or traffic management software. 3. Priority Queue: In a priority queue, each element is assigned a priority, and the element with the highest priority is dequeued first,

Chapter 7: Stacks Welcome to Chapter 7! In this chapter, we will delve deeply into the concept of Stacks, a fundamental data structure with a Last-In-First-Out (LIFO) ordering principle. Understanding stacks will equip you with a solid foundation for tackling a range of computational problems and algorithms. What is a Stack? A Stack is a collection of elements that follows the Last-In-First-Out (LIFO) principle. This means that the most recently added element is the first one to be removed. Think of it like a stack of plates: you add new plates on top and remove plates from the top. Here’s a basic diagram of a stack: Top -> [3] [2] [1] Bottom -> NULL In this diagram, 3 is the top element, and it will be removed first during a pop operation. Why Use Stacks? Stacks are versatile and used in many computing scenarios: Function Call Management: The call stack manages function calls and local variables during program execution. Expression Evaluation: Stacks are essential in evaluating arithmetic expressions, especially in postfix notation. Backtracking Algorithms: Used in algorithms like depth-first search where you need to backtrack through previous states. In a similar way, Emancipation Edutech Private Limited might use stacks to manage learning modules or track student progress, highlighting the versatility of this data structure. Basic Operations on Stacks Stacks support a few primary operations: Push: Add an element to the top of the stack. Pop: Remove the element from the top of the stack. Peek: View the top element without removing it. IsEmpty: Check if the stack is empty. Let’s look at how to implement these operations using both array-based and linked-list-based stacks. Stack Implementation Using an Array An array-based stack is straightforward and involves a fixed-size array to store elements. Here’s a detailed implementation: Define the Stack Structure #include <stdio.h> #include <stdlib.h> #define MAX 100 // Define the maximum size of the stack // Define the stack structure struct Stack { int top; int arr[MAX]; }; top: Index of the top element in the stack. arr: Array to hold stack elements. Initialize the Stack void initialize(struct Stack* s) { s->top = -1; // Stack is empty initially } Push Operation void push(struct Stack* s, int value) { if (s->top == MAX – 1) { // Check for stack overflow printf("Stack Overflow\n"); return; } s->arr[++(s->top)] = value; // Increment top and add value } Pop Operation int pop(struct Stack* s) { if (s->top == -1) { // Check for stack underflow printf("Stack Underflow\n"); return -1; // Return -1 to indicate an error } return s->arr[(s->top)–]; // Return top value and decrement top } Peek Operation int peek(struct Stack* s) { if (s->top == -1) { // Check if stack is empty printf("Stack is Empty\n"); return -1; // Return -1 to indicate an error } return s->arr[s->top]; // Return top value } Example Usage int main() { struct Stack stack; initialize(&stack); // Push elements push(&stack, 10); push(&stack, 20); push(&stack, 30); // Peek the top element printf("Top element is %d\n", peek(&stack)); // Pop elements printf("Popped element is %d\n", pop(&stack)); printf("Popped element is %d\n", pop(&stack)); // Check if the stack is empty if (s->top == -1) { printf("Stack is empty\n"); } return 0; } Output: Top element is 30 Popped element is 30 Popped element is 20 Stack is not empty Stack Implementation Using a Linked List A stack can also be implemented using a linked list, which allows for dynamic size adjustments. Define the Node Structure #include <stdio.h> #include <stdlib.h> // Define the node structure struct Node { int data; struct Node* next; }; // Define the stack structure struct Stack { struct Node* top; }; Initialize the Stack void initialize(struct Stack* s) { s->top = NULL; // Stack is empty initially } Push Operation void push(struct Stack* s, int value) { struct Node* newNode = (struct Node*) malloc(sizeof(struct Node)); newNode->data = value; newNode->next = s->top; s->top = newNode; } Pop Operation int pop(struct Stack* s) { if (s->top == NULL) { // Check if stack is empty printf("Stack Underflow\n"); return -1; // Return -1 to indicate an error } struct Node* temp = s->top; int value = temp->data; s->top = s->top->next; free(temp); return value; } Peek Operation int peek(struct Stack* s) { if (s->top == NULL) { // Check if stack is empty printf("Stack is Empty\n"); return -1; // Return -1 to indicate an error } return s->top->data; // Return top value } Example Usage int main() { struct Stack stack; initialize(&stack); // Push elements push(&stack, 10); push(&stack, 20); push(&stack, 30); // Peek the top element printf("Top element is %d\n", peek(&stack)); // Pop elements printf("Popped element is %d\n", pop(&stack)); printf("Popped element is %d\n", pop(&stack)); // Check if the stack is empty if (s->top == NULL) { printf("Stack is empty\n"); } return 0; } Output: Top element is 30 Popped element is 30 Popped element is 20 Stack is not empty Applications of Stacks Stacks are fundamental in various computing contexts: Expression Evaluation: Evaluate postfix expressions and manage operators and operands. Function Call Management: The call stack tracks function calls and local variables. Undo Mechanism: Manage undo operations in text editors and similar applications. For instance, Emancipation Edutech Private Limited might use stacks to manage undo operations in interactive learning tools or to handle complex navigation tasks efficiently. Advantages and Disadvantages of Stacks Advantages: Simple Operations: Push and pop operations are straightforward and easy to implement. Dynamic Size: Linked-list-based stacks can grow and shrink dynamically, avoiding fixed size limitations. Disadvantages: Fixed Size: Array-based stacks have a fixed size, which can lead to overflow if the limit is reached. Limited Access: Only the top element is accessible directly, which might not be suitable for all use cases. Summary of Chapter 7 In this chapter, we have explored Stacks in depth, including their basic operations, implementations using arrays and linked lists, and their various applications. Understanding stacks will help you handle problems requiring LIFO access efficiently. Key takeaways: Stacks operate on a Last-In-First-Out principle. Implementations can be array-based or linked-list-based. Stacks are used

Chapter 6: Circular Linked Lists Welcome to Chapter 6! In this chapter, we will explore Circular Linked Lists, a variation of linked lists where the last node in the list points back to the first node, forming a circular structure. This type of linked list is particularly useful in scenarios where you need a continuous loop through your data. What is a Circular Linked List? A Circular Linked List is a type of linked list in which the last node points back to the first node instead of having a NULL pointer. This circular structure allows you to traverse the list from any node and return to the starting node without having to check for the end. Here’s a graphical representation of a circular linked list: [Data | Next] -> [Data | Next] -> [Data | Next] -+ ^ | | | +——————————————-+ In this diagram, you can see that the next pointer of the last node points back to the first node, creating a loop. Why Use Circular Linked Lists? Circular linked lists are useful for scenarios that require a continuous loop or when you need to repeatedly cycle through the elements. Some applications include: Round-Robin Scheduling: In operating systems, circular linked lists can be used to manage tasks in a round-robin fashion. Circular Buffers: They are used in scenarios where you have a fixed-size buffer and need to overwrite old data when the buffer is full. Navigation Systems: For continuous navigation without end, like in a continuous data feed. Just like how Emancipation Edutech Private Limited might use circular linked lists to cycle through learning modules or resources efficiently, these structures can simplify repetitive tasks. Building a Circular Linked List To implement a circular linked list, we start by defining the node structure: #include <stdio.h> #include <stdlib.h> // Define a node structure struct Node { int data; struct Node* next; }; Creating a Node Creating a node is similar to singly linked lists, but with an additional step to handle the circular nature: struct Node* createNode(int value) { struct Node* newNode = (struct Node*) malloc(sizeof(struct Node)); newNode->data = value; newNode->next = newNode; // Point to itself initially return newNode; } Inserting Elements in a Circular Linked List Here’s how you can insert nodes into a circular linked list: At the Beginning: void insertAtBeginning(struct Node** head, int value) { struct Node* newNode = createNode(value); if (*head == NULL) { *head = newNode; newNode->next = *head; return; } struct Node* temp = *head; while (temp->next != *head) { temp = temp->next; } newNode->next = *head; temp->next = newNode; *head = newNode; } This function inserts a new node at the start of the circular list and adjusts the next pointers to maintain the circular structure. At the End: void insertAtEnd(struct Node** head, int value) { struct Node* newNode = createNode(value); if (*head == NULL) { *head = newNode; newNode->next = *head; return; } struct Node* temp = *head; while (temp->next != *head) { temp = temp->next; } temp->next = newNode; newNode->next = *head; } This function adds a new node to the end of the circular list by traversing to the last node and updating pointers. Inserting After a Given Node: void insertAfter(struct Node* prevNode, int value) { if (prevNode == NULL) { printf("The given previous node cannot be NULL"); return; } struct Node* newNode = createNode(value); newNode->next = prevNode->next; prevNode->next = newNode; } This function inserts a new node after a given node, adjusting the next pointers accordingly. Traversing a Circular Linked List Traversal in a circular linked list involves starting from the head and continuing until you return to the head: void traverse(struct Node* head) { if (head == NULL) return; struct Node* temp = head; do { printf("%d -> ", temp->data); temp = temp->next; } while (temp != head); printf("(back to head)\n"); } This function loops through the list and prints each node’s data until it returns to the head. Example: Creating and Traversing a Circular Linked List Here’s a sample program to create a circular linked list and traverse it: int main() { struct Node* head = NULL; // Inserting nodes insertAtEnd(&head, 10); insertAtEnd(&head, 20); insertAtBeginning(&head, 5); insertAtEnd(&head, 30); insertAfter(head->next, 15); // Insert 15 after the node with data 10 // Traverse the list traverse(head); return 0; } Output: 5 -> 10 -> 15 -> 20 -> 30 -> (back to head) Deleting Nodes in a Circular Linked List Deleting nodes in a circular linked list involves similar steps as in singly linked lists but requires updating the next pointers to maintain the circular structure: Deleting the First Node: void deleteFirstNode(struct Node** head) { if (*head == NULL) return; struct Node* temp = *head; if ((*head)->next == *head) { free(*head); *head = NULL; return; } struct Node* last = *head; while (last->next != *head) { last = last->next; } last->next = (*head)->next; *head = (*head)->next; free(temp); } Deleting the Last Node: void deleteLastNode(struct Node** head) { if (*head == NULL) return; struct Node* temp = *head; if ((*head)->next == *head) { free(*head); *head = NULL; return; } struct Node* prev = NULL; while (temp->next != *head) { prev = temp; temp = temp->next; } prev->next = *head; free(temp); } Deleting a Node by Value: void deleteNodeByValue(struct Node** head, int value) { if (*head == NULL) return; struct Node* temp = *head; struct Node* prev = NULL; do { if (temp->data == value) { if (prev == NULL) { // Node to be deleted is the head deleteFirstNode(head); return; } else { prev->next = temp->next; free(temp); return; } } prev = temp; temp = temp->next; } while (temp != *head); } Advantages and Disadvantages of Circular Linked Lists Advantages: Circular Traversal: Easily traverse through the list without needing to check for the end. Efficient for Round-Robin Scheduling: Ideal for scenarios where cyclic behavior is required. Disadvantages: Complexity: Slightly more complex to manage compared to singly linked lists. Potential for Infinite Loops: If not handled carefully, circular traversal can lead to infinite loops. Wrapping Up Chapter 6