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Some Important Linear Data Structures- at a glance
 StacksQueuesSingle Linked ListDouble Linked ListCircular Linked List

### To go through the C program / source-code, scroll down to the end of this page

Circular Linked List
Circular linked list is a more complicated linked data structure. In this the elements can be placed
anywhere in the heap memory unlike array which uses contiguous locations. Nodes in a linked
list are linked together using a next field, which stores the address of the next node in the next
field of the previous node i.e. each node of the list refers to its successor and the last node points
back to the first node unlike singly linked list. It has a dynamic size, which can be determined
only at run time.

#### Related Tutorials :

 Single Linked List A self referential data structure. A list of elements, with a head and a tail; each element points to another of its own kind. Double Linked List A self referential data structure. A list of elements, with a head and a tail; each element points to another of its own kind in front of it, as well as another of its own kind, which happens to be behind it in the sequence. Circular Linked List Linked list with no head and tail - elements point to each other in a circular fashion.

### Performance

1. The advantage is that we no longer need both a head and tail variable to keep track of
the list. Even if only a single variable is used, both the first and the last list elements can
be found in constant time. Also, for implementing queues we will only need one pointer
namely tail, to locate both head and tail.
2. The disadvantage is that the algorithms have become more complicated.

### Basic Operations on a Circular Linked List

Insert – Inserts a new element at the end of the list.
Delete – Deletes any node from the list.
Find – Finds any node in the list.
Print – Prints the list.

## Circular Linked List - C Program source code

`#include<stdio.h>#include<stdlib.h>typedef struct Node {        int data;        struct Node *next;}node;void insert(node *pointer, int data){        node *start = pointer;        /* Iterate through the list till we encounter the last node.*/        while(pointer->next!=start)        {                pointer = pointer -> next;        }        /* Allocate memory for the new node and put data in it.*/        pointer->next = (node *)malloc(sizeof(node));        pointer = pointer->next;        pointer->data = data;        pointer->next = start;}int find(node *pointer, int key){        node *start = pointer;        pointer =  pointer -> next; //First node is dummy node.        /* Iterate through the entire linked list and search for the key. */        while(pointer!=start)        {                if(pointer->data == key) //key is found.                {                        return 1;                }                pointer = pointer -> next;//Search in the next node.        }        /*Key is not found */        return 0;}void delete(node *pointer, int data){        node *start = pointer;        /* Go to the node for which the node next to it has to be deleted */        while(pointer->next!=start && (pointer->next)->data != data)        {                pointer = pointer -> next;        }        if(pointer->next==start)        {                printf("Element %d is not present in the list\n",data);                return;        }        /* Now pointer points to a node and the node next to it has to be removed */        node *temp;        temp = pointer -> next;        /*temp points to the node which has to be removed*/        pointer->next = temp->next;        /*We removed the node which is next to the pointer (which is also temp) */        free(temp);        /* Beacuse we deleted the node, we no longer require the memory used for it .            free() will deallocate the memory.         */        return;}void print(node *start,node *pointer){        if(pointer==start)        {                return;        }        printf("%d ",pointer->data);        print(start,pointer->next);}int main(){        /* start always points to the first node of the linked list.           temp is used to point to the last node of the linked list.*/        node *start,*temp;        start = (node *)malloc(sizeof(node));         temp = start;        temp -> next = start;        /* Here in this code, we take the first node as a dummy node.           The first node does not contain data, but it used because to avoid handling special cases           in insert and delete functions.         */        printf("1. Insert\n");        printf("2. Delete\n");        printf("3. Print\n");        printf("4. Find\n");        while(1)        {                int query;                scanf("%d",&query);                if(query==1)                {                        int data;                        scanf("%d",&data);                        insert(start,data);                }                else if(query==2)                {                        int data;                        scanf("%d",&data);                        delete(start,data);                }                else if(query==3)                {                        printf("The list is ");                        print(start,start->next);                        printf("\n");                }                else if(query==4)                {                        int data;                        scanf("%d",&data);                        int status = find(start,data);                        if(status)                        {                                printf("Element Found\n");                        }                        else                        {                                printf("Element Not Found\n");                        }                }        }}`
`MCQ Quiz on Circular Linked ListCompanion MCQ Quiz for Circular Linked Lists- test how much you know about the topic. Your score will be e-mailed to you at the address you provide.`
`MCQ Quiz- Circular Linked List `

#### Related Visualizations (Java Applet Visualizations for different kinds of Linked Lists) :

Lists : Linear data structures, contain elements, each of which point to the "next" in the sequence as demonstrated in the examples below ( Simple, Circular and Double Linked Lists are some common kinds of lists ) .  Additions and removals can be made at any point in the list - in this way it differs from stacks and queues.

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Some Important Data Structures and Algorithms, at a glance:

 Arrays : Popular Sorting and Searching Algorithms Bubble Sort Insertion Sort Selection Sort Shell Sort Merge Sort Quick Sort Heap Sort Binary Search Algorithm Basic Data Structures  and Operations on them Stacks Queues Single Linked List Double Linked List Circular Linked List
 Tree Data Structures Binary Search Trees Heaps Height Balanced Trees Graphs and Graph Algorithms Depth First Search Breadth First Search Minimum Spanning Trees: Kruskal Algorithm Minumum Spanning Trees: Prim's Algorithm Dijkstra Algorithm for Shortest Paths Floyd Warshall Algorithm for Shortest Paths Bellman Ford Algorithm Popular Algorithms in Dynamic Programming Dynamic Programming Integer Knapsack problem Matrix Chain Multiplication Longest Common Subsequence Greedy Algorithms Elementary cases : Fractional Knapsack Problem, Task Scheduling Data Compression using Huffman Trees

Tutorials on Sorting- at a glance

 Bubble Sort - One of the most elementary sorting algorithms to implement - and also very inefficient. Runs in quadratic time. A good starting point to understand sorting in general, before moving on to more advanced techniques and algorithms. A general idea of how the algorithm works and a the code for a C program.Insertion Sort - Another quadratic time sorting algorithm - an example of dynamic programming. An explanation and step through of how the algorithm works, as well as the source code for a C program which performs insertion sort.Selection Sort - Another quadratic time sorting algorithm - an example of a greedy algorithm. An explanation and step through of how the algorithm works, as well as the source code for a C program which performs selection sort.Shell Sort- An inefficient but interesting algorithm, the complexity of which is not exactly known.Merge Sort An example of a Divide and Conquer algorithm. Works in O(n log n) time. The memory complexity for this is a bit of a disadvantage.Quick Sort In the average case, this works in O(n log n) time. No additional memory overhead - so this is better than merge sort in this regard. A partition element is selected, the array is restructured such that all elements greater or less than the partition are on opposite sides of the partition. These two parts of the array are then sorted recursively.Heap Sort- Efficient sorting algorithm which runs in O(n log n) time. Uses the Heap data structure.Binary Search Algorithm- Commonly used algorithm used to find the position of an element in a sorted array. Runs in O(log n) time.

Basic Data Structures and Algorithms

Stacks Last In First Out data structures ( LIFO ). Like a stack of cards from which you pick up the one on the top ( which is the last one to be placed on top of the stack ). Documentation of the various operations and the stages a stack passes through when elements are inserted or deleted. C program to help you get an idea of how a stack is implemented in code.

Queues First in First Out data structure (FIFO). Like people waiting to buy tickets in a queue - the first one to stand in the queue, gets the ticket first and gets to leave the queue first. Documentation of the various operations and the stages a queue passes through as elements are inserted or deleted. C Program source code to help you get an idea of how a queue is implemented in code.

Single Linked List A self referential data structure. A list of elements, with a head and a tail; each element points to another of its own kind.

Double Linked List- A self referential data structure. A list of elements, with a head and a tail; each element points to another of its own kind in front of it, as well as another of its own kind, which happens to be behind it in the sequence.

Circular Linked List Linked list with no head and tail - elements point to each other in a circular fashion.

Binary Search Trees A basic form of tree data structures. Inserting and deleting elements in them. Different kind of binary tree traversal algorithms.

Heaps A tree like data structure where every element is lesser (or greater) than the one above it. Heap formation, sorting using heaps in O(n log n) time.

Height Balanced Trees - Ensuring that trees remain balanced to optimize complexity of operations which are performed on them.

Graphs

Depth First Search - Traversing through a graph using Depth First Search in which unvisited neighbors of the current vertex are pushed into a stack and visited in that order.

Breadth First Search - Traversing through a graph using Breadth First Search in which unvisited neighbors of the current vertex are pushed into a queue and then visited in that order.

Minimum Spanning Trees: Kruskal Algorithm- Finding the Minimum Spanning Tree using the Kruskal Algorithm which is a greedy technique. Introducing the concept of Union Find.

Minumum Spanning Trees: Prim's Algorithm- Finding the Minimum Spanning Tree using the Prim's Algorithm.

Dijkstra Algorithm for Shortest Paths- Popular algorithm for finding shortest paths : Dijkstra Algorithm.

Floyd Warshall Algorithm for Shortest Paths- All the all shortest path algorithm: Floyd Warshall Algorithm

Bellman Ford Algorithm - Another common shortest path algorithm : Bellman Ford Algorithm.

Dynamic Programming A technique used to solve optimization problems, based on identifying and solving sub-parts of a problem first.

Integer Knapsack problemAn elementary problem, often used to introduce the concept of dynamic programming.

Matrix Chain Multiplication Given a long chain of matrices of various sizes, how do you parenthesize them for the purpose of multiplication - how do you chose which ones to start multiplying first?

Longest Common Subsequence Given two strings, find the longest common sub sequence between them.

Elementary cases : Fractional Knapsack Problem, Task Scheduling - Elementary problems in Greedy algorithms - Fractional Knapsack, Task Scheduling. Along with C Program source code.

Data Compression using Huffman TreesCompression using Huffman Trees. A greedy technique for encoding information.