data structure linked list assignment

Data Structures Explained with Examples - Linked List

Just like a garland is made with flowers, a linked list is made up of nodes. We call every flower on this particular garland to be a node. And each of the node points to the next node in this list as well as it has data (here it is type of flower).

Singly Linked List

Singly linked lists contain nodes which have a data field as well as a next field, which points to the next node in the sequence. Operations that can be performed on singly linked lists are insertion, deletion and traversal.

Internal implementation of CPython, the frames and evaluated variables are kept on a stack.

For this we need to iterate only forward aur get the head, therefore singly linked-list is used.

Doubly Linked List

Doubly linked lists contain node which have data field, next field and another link field prev pointing to the previous node in the sequence.

The browser cache which allows you to hit the BACK and FORWARD button. Here we need to maintain a doubly linked list, with URLs as data field, to allow access in both direction. To go to previous URL we will use prev field and to go to next page we will use next field.

Circular Linked List

Circular linked lists is a singly linked list in which last node, next field points to first node in the sequence.

Timesharing problem solved by the operating system.

In a timesharing environment, the operating system must maintain a list of present users and must alternately allow each user to use a small portion of CPU time, one user at a time. The operating system will pick a user, let him/her use a small amount of CPU time and then move on to the next user.

For this application, there should be no NULL pointers unless there is absolutely no one requesting CPU time, i.e list is empty.

Basic Operations

To add a new element to the list.

Insertion at the beginning:

• Create a new node with given data.
• Point new node’s next to old head .
• Point head to this new node.

Insertion in the middle/end.

Insertion after node X.

• Point new node’s next to old X’s next .
• Point X’s next to this new node.

Time Complexity: O(1)

To delete existing element from the list.

Deletion at the beginning

• Get the node pointed by head as Temp.
• Point head to Temp’s next .
• Free memory used by Temp node.

Deletion in the middle/end.

Deletion after node X.

• Get the node pointed by X as Temp.
• Point X’s next to Temp’s next .

To travel across the list.

• Get the node pointed by head as Current.
• Check if Current is not null and display it.
• Point Current to Current’s next and move to above step.

Time Complexity: O(n) // Here n is size of link-list

Implementation

C++ implementation of singly linked list, printing data in each node, insertion at the beginning, deletion after a node, python implementation of singly linked list.

• Linked lists are a dynamic data structure, which can grow and shrink, allocating and deallocating memory while the program is running.
• Insertion and deletion of node are easily implemented in a linked list at any position.

Disadvantages

• They use more memory than arrays because of the memory used by their pointers ( next and prev ).
• Random access is not possible in linked list. We have to access nodes sequentially.
• It’s more complex than array. If a language supports array bound check automatically, Arrays would serve you better.

We have to use free() in C and delete in C++ to free the space used by deleted node, whereas, in Python and Java free space is collected automatically by garbage collector.

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• What is an algorithm?
• Data Structure and Types
• Why learn DSA?
• Asymptotic Notations
• Master Theorem
• Divide and Conquer Algorithm

Data Structures (I)

• Types of Queue
• Circular Queue
• Priority Queue

Data Structures (II)

• Linked List
• Linked List Operations
• Types of Linked List
• Heap Data Structure
• Fibonacci Heap
• Decrease Key and Delete Node Operations on a Fibonacci Heap

Tree based DSA (I)

• Tree Data Structure
• Tree Traversal
• Binary Tree
• Full Binary Tree
• Perfect Binary Tree
• Complete Binary Tree
• Balanced Binary Tree
• Binary Search Tree

Tree based DSA (II)

• Insertion in a B-tree
• Deletion from a B-tree
• Insertion on a B+ Tree
• Deletion from a B+ Tree
• Red-Black Tree
• Red-Black Tree Insertion
• Red-Black Tree Deletion

Graph based DSA

• Graph Data Structure
• Spanning Tree
• Strongly Connected Components
• Adjacency Matrix
• Adjacency List
• DFS Algorithm
• Breadth-first Search
• Bellman Ford's Algorithm

Sorting and Searching Algorithms

• Bubble Sort
• Selection Sort
• Insertion Sort
• Counting Sort
• Bucket Sort
• Linear Search
• Binary Search

Greedy Algorithms

• Greedy Algorithm
• Ford-Fulkerson Algorithm
• Dijkstra's Algorithm
• Kruskal's Algorithm
• Prim's Algorithm
• Huffman Coding
• Dynamic Programming
• Floyd-Warshall Algorithm
• Longest Common Sequence

Other Algorithms

• Backtracking Algorithm
• Rabin-Karp Algorithm

DSA Tutorials

Doubly Linked List

Circular Linked List

Types of Linked List - Singly linked, doubly linked and circular

Linked list Data Structure

• Binary Search Tree(BST)

Linked List Operations: Traverse, Insert and Delete

There are various linked list operations that allow us to perform different actions on linked lists. For example, the insertion operation adds a new element to the linked list.

Here's a list of basic linked list operations that we will cover in this article.

• Traversal - access each element of the linked list
• Insertion - adds a new element to the linked list
• Deletion - removes the existing elements
• Search - find a node in the linked list
• Sort - sort the nodes of the linked list

Before you learn about linked list operations in detail, make sure to know about Linked List first.

Things to Remember about Linked List

• head points to the first node of the linked list
• next pointer of the last node is NULL , so if the next current node is NULL , we have reached the end of the linked list.

In all of the examples, we will assume that the linked list has three nodes 1 --->2 --->3 with node structure as below:

• Traverse a Linked List

Displaying the contents of a linked list is very simple. We keep moving the temp node to the next one and display its contents.

When temp is NULL , we know that we have reached the end of the linked list so we get out of the while loop.

The output of this program will be:

• Insert Elements to a Linked List

You can add elements to either the beginning, middle or end of the linked list.

1. Insert at the beginning

• Allocate memory for new node
• Change next of new node to point to head
• Change head to point to recently created node

2. Insert at the End

• Traverse to last node
• Change next of last node to recently created node

3. Insert at the Middle

• Allocate memory and store data for new node
• Traverse to node just before the required position of new node
• Change next pointers to include new node in between
• Delete from a Linked List

You can delete either from the beginning, end or from a particular position.

1. Delete from beginning

• Point head to the second node

2. Delete from end

• Traverse to second last element
• Change its next pointer to null

3. Delete from middle

• Traverse to element before the element to be deleted
• Change next pointers to exclude the node from the chain
• Search an Element on a Linked List

You can search an element on a linked list using a loop using the following steps. We are finding item on a linked list.

• Make head as the current node.
• Run a loop until the current node is NULL because the last element points to NULL .
• In each iteration, check if the key of the node is equal to item . If it the key matches the item, return true otherwise return false .
• Sort Elements of a Linked List

We will use a simple sorting algorithm, Bubble Sort , to sort the elements of a linked list in ascending order below.

• Make the head as the current node and create another node index for later use.
• If head is null, return.
• Else, run a loop till the last node (i.e. NULL ).
• In each iteration, follow the following step 5-6.
• Store the next node of current in index .
• Check if the data of the current node is greater than the next node. If it is greater, swap current and index .

Check the article on bubble sort for better understanding of its working.

• LinkedList Operations in Python, Java, C, and C++

Table of Contents

• Introduction

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5 Linked Lists

Learning Objectives

After reading this chapter you will…

• begin to understand how differences in data structures result in trade-offs and help when choosing which to apply in a real-world scenario.
• begin to use links or references to build more complex data structures.
• grasp the power and limitations of common arrays.

Introduction

You have a case of cola you wish to add to your refrigerator. Your initial approach is to add all colas to the refrigerator while still in the box. That way, when you want to retrieve a drink, they are all in the same place, making them easier to find. You will also know when you are running low because the box will be nearly empty. Storing them while still in the box clearly has some benefits. It does come with one major issue though. Consider a refrigerator filled with groceries. You may not have an empty spot large enough to accommodate the entire case of cola. However, if you open the case and store each can individually, you can probably find some spot for each of the 12 cans. You have now found a way to keep all cans cold, but locating those cans is now more difficult than it was before. You are now faced with a trade-off: Would you rather have all cans cold at the cost of slower retrieval times or all cans warm on the counter with faster retrieval times? This leads to judgment calls, like deciding between how much we value a cold cola and how quickly we need to retrieve one.

Our case of cola is like a data structure, and storing all cans in the box is analogous to an array. Just like the analogy, let us start by listing some of the desirable characteristics of arrays.

• We know exactly how many elements reside in them, both now and in the future. We know this because (in most languages) we are required to specify the length explicitly. Also, most implementations of arrays do not allow us to simply resize as needed. As we will see soon, this can be both a beneficial feature and a constraint.
• They are fast. Arrays are indexable data structures with lookups in constant time. In other words, if you have an array with 1,000 elements, getting the value at index 900 does not mean that you must first look at the first 899 elements. We can do this because array implementations rely on storage within contiguous blocks of memory. Those contiguous blocks have predictable and simple addressing schemes. If you are standing at the beginning of an array (say at address 0X43B), you can simply multiply 900 by the size of the element type stored in the array and look up the memory location that is that distance from the starting point. This can be done in constant time, O(1).

These desirable characteristics are also constraints if you look at them from a different perspective.

• Having an explicit length configured before you use the array does mean that we know the length without having to inspect the data structure, but it also means that we cannot add any new elements once we reach the capacity of the array. For plenty of applications, we may not know the proper size before we begin processing.
• Arrays are fast because they are stored in contiguous blocks of memory. However, for really large sets of data, it may be expensive (regarding time) or impossible (regarding space) to find a sufficiently large contiguous block of memory. In these cases, an array may perform poorly or not at all.

It is clear that, under certain circumstances, arrays may not serve all our needs. We now have a motivation for new types of data structures, which bring with them new trade-offs. The first of these new data structures that we will consider is the linked list.

Structure of Linked Lists

Linked lists are the first of a series of reference-based data structures that we will study. The main difference between arrays and linked lists is how we define the structure of the data. With arrays, we assumed that all storage was contiguous. To locate the value at index 5, we simply take the address of the beginning of the array and add 5 times the size of the data type we are storing. That gives us the address of the data we wish to retrieve. Of course, most modern languages give us simpler indexing operators to accomplish the task, but the description above is essentially what happens at a lower level.

Linked lists do not use contiguous memory, which implies that some other means of specifying a sequence must be used. Linked lists (along with trees and graphs in later chapters) all use the concept of a node, which references other nodes. By leveraging these references and adding a value (sometimes called a payload), we can create a data structure that is distinctive from arrays and has different trade-offs.

When working with the linked list, the next element in the structure, starting from a given element, is determined by following the reference to the next node. In the example below, node a references (or points to) node b. To determine the elements in the structure, you can inspect the payloads as you follow the references. We follow these references until we find a null reference (or a reference that points to nothing). In this case, we have a linked list of length 2, which has the value 1 followed by the value 12.

From a practical standpoint, implementations require that the chosen language have some sort of feature that allows for grouping the value with the reference. Most languages will accomplish this task with either structs or objects. For pseudocode examples, we will assume the following definition of a node. The payload is of type integer because it is convenient for the remainder of the chapter, but the data stored in the node can be of any type that is useful given some real-world circumstances.

Let us consider the following explicitly defined list with powers of 3 as the values. The choice of values is intended to reinforce the sequential nature of the data structure and could have easily been any other well-known sequence. The critical step is how the Next reference is assigned to subsequent nodes in lines 9, 10, and 11. Later, we will define a procedure for inserting values at an arbitrary position, but for now, we will use a as our root. The resulting linked list is depicted in figure 5.3.

Operations on Linked Lists

Lookup (list traversal).

Continuing the comparison with arrays, our first task will be to look up the value at an arbitrary position relative to a particular node in a linked list. You may consider a position in a linked list as the analogue of an array’s index. It is the ordinal number for a specific value or node within the data structure. For the sake of consistency, we will use 0-based positions.

We address lookup first because it most clearly illustrates the means by which we traverse the linked list. On line 2, we start with some node, then lines 3 and 4 step forward to the next node an appropriate number of times. Whenever we wish to insert, delete, or look up a value or simply visit each node, we must either iteratively or recursively follow the Next reference for the desired number of sequential nodes.

Now that we have a means of looking up a value at an arbitrary position, we must consider how it performs. We start again by considering arbitrary index lookups in arrays. Recall that a lookup in an array is actually a single dereference of a memory address. Because dereferencing on an array is not dependent on the size of the array, the runtime for array lookups is O(1). However, to look up an arbitrary element in a linked list (say, the nth element), we must dereference n − 1 different addresses as we follow the Next reference. We now have a number of dereferences dependent on the length of the linked list. Specifically, our cost function of the worst-case scenario will be f(n) = n − 1, where n is the length, which is clearly O(n). Dereferencing within some loop or with recursion is a featured pattern in nearly every linked list algorithm. Therefore, in most cases, we will expect these algorithms to run in O(n) time.

Length (and Additional Means of Traversal)

While it is the case that most list traversals are implemented with for-loops, there are occasions where other styles of traversal are more appropriate. For example, for-loops are ill-suited for scenarios where we do not know exactly how many times we must loop. As a result, while-loops are often used whenever all nodes must be visited. Consider the function below, which returns an integer representing the number of elements in the list starting at rootNode :

Also worth noting is that, due to the self-referencing definition of the Node class, many list procedures are reasonably implemented using recursion. If you consider a given root node, the length of a linked list starting at that node will be the sum of 1 and the length of the list starting at the Next reference (the recursive case). The length of a list starting with a null node is 0 (the base case).

As we explore more algorithms in this book, we will discover that often recursive solutions drastically reduce the complexity of our implementation. However, we should pay close attention here. Because we dereference the Next node for every node visited, our solution still runs in O(n) time.

To create a general-purpose data structure, our next operation will be to insert new values at arbitrary positions within the linked list. For the sake of simplicity, this function assumes that the position is valid for the current length of the linked list.

When reading and trying to comprehend this algorithm, we should pay close attention to three key things:

• We again see a linear traversal through the list to a given position by traversing the Next reference. As usual, it does not matter whether this is achieved with a for-loop, a while-loop, or recursion, the result is still the same. We maneuver through a list by starting at some point and following the references to other nodes. This is an incredibly important concept, as it lays the foundation for more interesting and useful data structures later in this book.
• When implementing algorithms, edge cases sometimes require specific attention. This function is responsible for inserting the value at a desired position, regardless of whether that position is 0, 2, or 200. Inserting a value into the middle of a linked list means that we must set references for the prior’s Next as well as newNode.Next . Inserting at position 0 is fundamentally different in that there is no prior node.
• More so than other statements, lines 14 and 15 may feel interchangeable. They are not. Much like the classic exchange exercise in many programming textbooks, executing these statements in the reverse order will lead to different behavior. It is a worthwhile exercise to consider what the outcome would be if they were switched.

We can visually trace the following example of an insertion:

Next, we come to the runtime analysis of this function. Due to the linear traversal, we consider the algorithm itself to be of O(n) regardless of whether we are inserting at position 2 or 200. However, what if we want to insert at position 0? In this case, the number of operations required is not dependent on the length of the linked list, and therefore this specific case runs in O(1). When studying algorithms, we typically categorize using the worst-case scenario but may specify edge-case runtimes when appropriate. In other words, if we only ever care about inserting at the front of a linked list, we may consider this special case of insert to be an O(1) operation.

Now that we have seen how to traverse the list via the Next reference and rearrange those references to insert a new node, we are ready to address removal of nodes. We will continue to provide the root node and a position to the function. Also, because we might choose to remove the element at the 0 position, we will continue to return the root node of the resulting list. As with the insertAtPosition , we assume that the value for position is valid for this list.

The result of the diagram above is that a traversal of the linked list will indeed include the values 8, 9, and 11 as desired. We should pay close attention to the node with value 10. Depending on the language we choose to implement linked lists, we may or may not be required to address this removed node. If the language’s runtime is memory managed, you may simply ignore the unreferenced node. In languages that rely on manual memory management, you should be prepared to deallocate the storage. The runtime for the algorithm, as a function of the length of the list, is still O(n), with the special case of position as 0 running again in constant time.

Doubly Linked Lists

Consider a scenario where we want to track the sequence of changes to a shared document. Compared to arrays, a linked list can grow as needed and is better suited for the task. We choose to inspect the change at position 500 in the linked list at a cost of 499 dereferences. We then realize we stepped two changes too far. We are actually concerned with change 498. We must then incur a cost of 497 dereferences to simply move backward two steps. The issue is that our nodes currently only point to the next value in the sequence and not the previous. Luckily, we can simply choose to include a Prior reference.

The choice to track prior nodes in addition to the next nodes does come with trade-offs. First, the size of each node stored is now larger. If we assume an integer is the same size as a reference, we have likely increased the size of each node stored by 50%. Depending on the needs of the application, constraints of the physical device, and size of the linked list, this increase may or may not be acceptable.

We also have more complicated (and technically slightly slower) functions for insertion and removal of nodes. See the pseudocode below for considerations in the general case. The case when position is 0 has also been omitted. Completing that case is an exercise at the end of this chapter.

Reference Reassignment for Singly Linked List Insert

Reference Reassignment for Doubly Linked List Insert

If we would like to make use of a Prior reference, we now must maintain that value on each insertion and removal. At times it is as easy as setting a value on an object (line 2). Other times we have to introduce a new condition (line 4). In this case, we check newNode.Next against null because we may be inserting our new value at the end of the list, in which case, there will not be any node with Prior set to newNode . Doubly linked list insert now requires as many as 5 operations where we only had 2 for singly linked lists. While this does mean that doubly linked list insert is technically slower, we only perform these operations once per function call. As a result, we have two functions that run in O(n) even though one is technically faster than the other.

Returning to the change tracking example at the beginning of this section, we now have a means of moving forward and backward through our list of changes. If we wish to start our analysis of the change log at entry 500, it will indeed cost us 499 dereferences to reach that node. However, once at that node, we can inspect entry 498 with a cost of 2 dereferences by following the Prior reference.

Augmenting Linked Lists

Just as we saw when inserting or removing at position 0, we can often find clever ways to improve certain behaviors of linked lists. These improvements may lead to better runtimes or simply have a clearer intent. In this section, we will consider the most common ways linked lists are augmented. Generally speaking, we will follow the same strategy used for doubly linked lists. The main principle is this: At the time that we know some useful bit of information, we will choose to simply save it for later. This will lead to a marginally higher cost for certain operations and a larger amount of data to store, but certain operations will become much faster.

So far in this chapter, we have implicitly defined a list as a single node representing the root element. To augment our data structure, we will now more formally define the full concept of a list as follows. For the definition of this class, we can choose to use a singly or doubly linked node. For simplicity, examples in this section return to using a singly linked node.

As was the case with doubly linked lists, our insert and remove code is now substantially more complicated. One nice benefit is that we now modify the list object and no longer need to return the root node.

For each insert into the list, we must now maintain some new values on the list object. Lines 7, 8, 11, and 22 help keep track of when we have changed the head or tail of the list. Line 24 runs regardless of where the value was inserted because we now have one more element in the list.

This extra work was not in vain. Consider what was required if we wanted to write a function to return the last element of a linked list represented by the root node compared to running it on a list object.

Last Element Using Root Node and Next References

Last Element Using the List and Tail References

The same improvements can be seen in retrieving the length of a list.

Length Using Root Node and Next References

Length Using the List and Length Values

Abstract Data Types

Before closing this chapter on linked lists, we benefit from considering abstractions. An abstract data type (ADT) is a collection of operations we want to perform on a given data type. Just as we can imagine numerous implementations for a given data structure (maybe we change a for-loop to a while-loop or recursion), we can also imagine numerous data structures that satisfy an ADT.

Consider the operations defined above for linked lists. We often want to insert, delete, count, and iterate over list elements. We call this set of operations a list ADT. A list may be implemented using linked nodes, an array, or some other means. However, it must provide these four operations. Implied in this description of ADTs is the fact that we cannot discuss the asymptotic runtime or space requirements of an ADT. Without knowing how the ADT is implemented, we cannot conclude much (if anything) about the runtime. For example, we could conclude that iteration is no better than O(n) because iteration requires us to touch each element regardless of implementation. We could not determine anything about the runtime of lookup because an array would be O(1), a linked list O(n), and a skip list O(log n). This last data structure is not formally covered in this chapter.

In subsequent chapters, we will at times refer to ADTs without specifying the precise data structure. In doing so, we will be able to focus on the new data structure without concern for how the ADT is implemented. Naturally, when we address the runtimes and space utilization of those algorithms, we must choose between data structures.

• Write three functions that print all values in a singly linked list. Write one using each of the following: for-loop, while-loop, and recursion.
• Write a removeAtPosition function for a doubly linked list that correctly maintains the Prior reference when the removal occurs at position 0, length − 1, or some arbitrary position in between.
• Write a removeAtPosition function for a singly linked list that correctly maintains the Head and Tail references when the removal occurs at position 0, length − 1, or some arbitrary position between.

Cormen, Thomas H., Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms , 2nd ed. Cambridge, MA: The MIT Press, 2001.

An Open Guide to Data Structures and Algorithms by Paul W. Bible and Lucas Moser is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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4. 5. Linked Lists ¶

4. 5.1. linked lists ¶.

In this module we present one of the two traditional implementations for lists, usually called a linked list . The linked list uses dynamic memory allocation , that is, it allocates memory for new list elements as needed. The following diagram illustrates the linked list concept. There are three nodes that are “linked” together. Each node has two boxes. The box on the right holds a link to the next node in the list. Notice that the rightmost node has a diagonal slash through its link box, signifying that there is no link coming out of this box.

Because a list node is a distinct object (as opposed to simply a cell in an array), it is good practice to make a separate list node class. This can either be a standalone class or an inner class, and both have their advantages and disadvantages.

The list built from such nodes is called a singly linked list , or a one-way list , because each list node has a single pointer to the next node on the list.

Our class for linked lists contains two private variables, one pointer to the head of the list, and a variable storing the number of elements. (This second variable is in theory unnecessary, but it improves the efficiency of getting the list size).

Here is our implementation for list nodes, an inner private class Node . Objects in the Node class contain a field elem to store the element value, and a field next to store a pointer to the next node on the list.

4. 5.1.1. Getting and setting values ¶

If we want to get or set the value at a certain index, we simply iterate through the nodes in sequence until we get to the node we want.

4. 5.2. Adding and removing nodes ¶

However, if we want to add or remove nodes, there is a problem with using a pointer to the current node.

So, using a current pointer, it is possible to add and remove nodes, using some complicated coding. But this does not work for the very last node! There are several possible ways to deal with this problem. One is to always have an empty node (a “dummy node”) at the very end of the list, but this will increase memory usage.

Another simple solution is to have a pointer to the node before the current node. This is the solution we will adopt.

4. 5.3. Adding a Node ¶

Here are some special cases for linked list insertion: Inserting at the beginning of a list, and appending at the end.

Here’s the code for addition.

Here’s an exercise for adding a value to a linked list.

4. 5.4. Removing a Node ¶

Here’s the code for deletion:

And here’s an exercise.

4. 5.5. Complexity analysis ¶

Locating a certain position \(i\) in the list requires \(i\) steps. The worst case is if we want to go to the last node, so the time complexity for above all operations is \(\Theta(n)\) .

This is much worse than the array-based list , where these operations are \(\Theta(1)\) . So are linked lists totally useless? No! But they don’t work well with our current List interface.

To make linked lists useful, we need an enhanced iterator interface, where we can move forwards and backwards in the list, and add/remove nodes through this enhanced iterator. In the standard Java API, this kind of iterator is called a ListIterator , which is part of Java’s standard LinkedList .

4. 5.6. Linked List: Full code ¶

Finally, here is the full source code for the class LinkedList .

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Linked List

A Linked List is a data structure used for storing collections of data. Unlike an array, which stores data contiguously in memory, a Linked List stores references to its elements.

A single element in a Linked List, typically called a “node,” holds both the data and a reference (or “link”) to the next node in the sequence.

Types of Linked Lists

• Singly Linked List : Each node has a link to the next node.
• Doubly Linked List : Each node has links to both the next and previous nodes.
• Circular Linked List : The last node in the list points back to the first node instead of having a null reference.
• Dynamic size
• Efficient insertions and deletions

Disadvantages

• No direct access to random elements
• Extra memory required for storing node references

Implementing (Singly) Linked List in Java

Usage of linked lists.

Linked lists are used in various scenarios for different advantages they offer:

• Dynamic Memory Allocation : Unlike arrays, they can easily grow or shrink in size, allocating memory as needed.
• Efficient Insertions/Deletions : Adding or removing an element doesn’t require shifting, unlike arrays, making these operations quicker.
• Implementation of Other Data Structures : Used as building blocks for more complex data structures like stacks, queues, and graphs.
• Memory Utilization : Good option when you have a limited understanding of the data you need to store and want to use memory more efficiently.
• Sequence Maintenance : Used in scenarios like maintaining a playlist where the order of elements can change frequently.
• Polynomial Representation : Used in computer algebra systems for representing polynomials.
• Page Replacement Algorithms : In Operating Systems, used in Least Recently Used (LRU) cache scheme.
• Undo Functionality : In text editors, to keep track of editing history for undo operations.
• Sparse Data Structures : Useful in creating sparse arrays or matrices.

Java and Singly Linked Lists: Using Built-in Constructs

Java provides an in-built class to deal with the singly linked list data structure. Let’s explore it in detail!

1. LinkedList Class :

Although Java’s LinkedList class is implemented as a doubly-linked list, its functionality and methods can be used to operate as a singly linked list. It resides in the java.util package.

Key Methods :

• add(E e) : Appends the specified element to the end of the list.
• add(int index, E element) : Inserts the specified element at the specified position in the list.
• get(int index) : Returns the element at the specified position in the list.
• remove() : Retrieves and removes the head (first element) of the list.
• remove(int index) : Removes the element at the specified position.
• size() : Returns the number of elements in the list.

2. Using LinkedList as a Singly Linked List :

Though LinkedList is inherently doubly-linked, we can restrict our interactions to make it behave like a singly linked list.

3. Points to Note :

• While LinkedList is a doubly-linked list at its core, by only using certain methods and avoiding direct navigation to previous nodes, you can use it as a singly linked list.
• Java’s standard library doesn’t provide a dedicated singly linked list implementation because the LinkedList class is versatile and covers both single and doubly linked list use cases.
• For most applications, the overhead of a doubly-linked list in the LinkedList class is negligible, but if the overhead becomes a concern, you might need to implement a custom singly linked list.

In summary, Java’s LinkedList class can serve as a handy tool for singly linked list operations, even though it’s inherently doubly-linked.

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• Data Structures & Algorithms
• DSA - Overview
• DSA - Environment Setup
• DSA - Algorithms Basics
• DSA - Asymptotic Analysis
• Data Structures
• DSA - Data Structure Basics
• DSA - Data Structures and Types
• DSA - Array Data Structure
• Linked Lists
• DSA - Linked List Data Structure
• DSA - Doubly Linked List Data Structure
• DSA - Circular Linked List Data Structure
• Stack & Queue
• DSA - Stack Data Structure
• DSA - Expression Parsing
• DSA - Queue Data Structure
• Searching Algorithms
• DSA - Searching Algorithms
• DSA - Linear Search Algorithm
• DSA - Binary Search Algorithm
• DSA - Interpolation Search
• DSA - Jump Search Algorithm
• DSA - Exponential Search
• DSA - Fibonacci Search
• DSA - Sublist Search
• DSA - Hash Table
• Sorting Algorithms
• DSA - Sorting Algorithms
• DSA - Bubble Sort Algorithm
• DSA - Insertion Sort Algorithm
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• DSA - Shell Sort Algorithm
• DSA - Heap Sort
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• Graph Data Structure
• DSA - Graph Data Structure
• DSA - Depth First Traversal
• DSA - Breadth First Traversal
• DSA - Spanning Tree
• Tree Data Structure
• DSA - Tree Data Structure
• DSA - Tree Traversal
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• DSA - Divide and Conquer
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• DSA - Strassen's Matrix Multiplication
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• Greedy Algorithms
• DSA - Greedy Algorithms
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• DSA - Prim's Minimal Spanning Tree
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Data Structure - Linked List

Linked list basics.

A linked-list is a sequence of data structures which are connected together via links.

Linked List is a sequence of links which contains items. Each link contains a connection to another link. Linked list the second most used data structure after array. Following are important terms to understand the concepts of Linked List.

Link − Each Link of a linked list can store a data called an element.

Next − Each Link of a linked list contain a link to next link called Next.

LinkedList − A LinkedList contains the connection link to the first Link called First.

Linked List Representation

As per above shown illustration, following are the important points to be considered.

• LinkedList contains an link element called first.
• Each Link carries a data field(s) and a Link Field called next.
• Each Link is linked with its next link using its next link.
• Last Link carries a Link as null to mark the end of the list.

Types of Linked List

Following are the various flavours of linked list.

Simple Linked List − Item Navigation is forward only.

Doubly Linked List − Items can be navigated forward and backward way.

Circular Linked List − Last item contains link of the first element as next and and first element has link to last element as prev.

Basic Operations

Following are the basic operations supported by a list.

Insertion − add an element at the beginning of the list.

Deletion − delete an element at the beginning of the list.

Display − displaying complete list.

Search − search an element using given key.

Delete − delete an element using given key.

Insertion Operation

Insertion is a three step process −

• Create a new Link with provided data.
• Point New Link to old First Link.
• Point First Link to this New Link.

Deletion Operation

Deletion is a two step process −

• Get the Link pointed by First Link as Temp Link.
• Point First Link to Temp Link's Next Link.

Navigation Operation

Navigation is a recursive step process and is basis of many operations like search, delete etc. −

• Get the Link pointed by First Link as Current Link.
• Check if Current Link is not null and display it.
• Point Current Link to Next Link of Current Link and move to above step.

Note −

Advanced Operations

Following are the advanced operations specified for a list.

Sort − sorting a list based on a particular order.

Reverse − reversing a linked list.

Sort Operation

We've used bubble sort to sort a list.

Reverse Operation

Following code demonstrate reversing a single linked list.

To see linked-list implementation in C programming language, please click here .

CS 314 - Specification 5 - Linked Lists

Programming Assignment 5:  Individual Assignment. You must complete this assignment on your own. You may not acquire from any source (e.g.  another student or a web site) a partial or complete solution to a problem or project that has been assigned. You may not show another student your solution to an assignment. You may not have another person (current student, former student, tutor, friend, anyone) “walk you through” how to solve an assignment. You may get help from the instructional staff. You may discuss general ideas and approaches with other students but you may not develop code together. Review the class policy on collaboration from the syllabus .

• Placed online: Wednesday, February 21
• 20 points, ~2% of final grade.
• Do not later than 11 pm on Thursday, February 29

The purpose of this assignment is to implement a linked list class that uses doubly linked nodes. Doubly linked nodes have references to the next node in the list and back to the previous node in the list . This makes some things harder because there are more references to set up so more chances for logic errors. But, it makes a lot of things easier because it is possible to move backwards in the linked structure of nodes without having to start over at the beginning, use the look ahead technique, or use a trailer node.

Draw pictures!! When completing methods and figuring out all the references that have to be updated and what the special cases are DRAW PICTURES of the linked structure. This is much easier than just looking at code and trying to determine (guess?) what must be done or why something does not work. Don't program by permutation or try cargo cult programming .

Please realize, when you get an error in a test the cause is typically in another method. It is often the case that students make errors when adding or removing elements or changing the structure of the internal storage container (the linked structure of nodes), but the error does not manifest itself until later on in a test of toString.

Implementation details:

Complete the LinkedList class.  For this assignment you may not use the LinkedList or ArrayList classes from the Java standard library. You must implement the underlying storage container as a doubly linked structure using the DoubleListNode class. The DoubleListNode s and LinkedList are generic based on Java's generic syntax.

You may approach the implementation in many ways. (References to first and last node, circular list with only a link to the first node, use of header nodes that don't contain any data) The approach you take is up to you. Each has certain advantages and disadvantages.

In addition to implementing the methods specified in the IList interface you must add  the following methods to the LinkedList class. E is the data type variable. It will hold what the data type is for the elements (thus the E) of a particular LinkedList object.

• void addFirst(E item) // add an item to the front of this LinkedList
• void addLast(E item) // add an item to the end of this LinkedList
• E removeFirst() // remove the first item in this LinkedList
• E removeLast() // remove the first item in this LinkedList
• override the toString method. The data in the list should be listed between square brackets with a comma between each item and a single space after each comma. e.g. [A, B, C] public String toString()
• override the equals method. Two IList s are equal if they have the same number of elements in the same order. public boolean equals(Object other). Two empty lists are equal regardless of the kinds of elements they store. (This is true in the Java standard library.) Also note, we are implementing the IList interface. To meet the requirement IList puts on classes that implement it, your LinkedList is considered equal to any other IList (not just a LinkedList) if it has the same elements in the same order.

LinkedListTester provides some tests for the LinkedList class. In your final submission delete the provided tests. Add at least 2 tests of your own per method in the LinkedList class to the LinkedListTester class. Delete the provided tests. You may post the tests you write to the class class discussion group, but in the end you must implement your own tests.

  Note, most of my tests rely on your iterator method. If that is not working you will fail those tests. I strongly recommend you test your methods as you complete them by writing your own tests! Write a method, then test it. Write a method, then test it. DON'T do this:  write a method, write a method, write a method, ... then test ...then debug, debug, debug, debug, debug, debug, debug, debug, debug, debug, debug, debug.

For every method in the LinkedList class state the Big O of the method if there are already N items in the list.

The methods for the iterator for your linked list shall all be O(1).  Implement the hasNext and next methods for your iterator. You shall replace the default remove method from the Iterator interface with a remove method that actually removes the last element returned by next. Your iterator class does NOT need to detect co-modification errors.

If a method has preconditions you must check those preconditions and throw an Exception if they are not met.

You can, of course, implement methods using other methods from the class. You should only do this if the version of a method that relies on other methods in the class is as efficient as not using other methods.

As always, use good style: comments for non obvious algorithms and operations, good variable names, use helper methods if necessary, make operations as efficient as possible given the constraints.

Experiments: It is interesting and important to know the difference in behavior between linked lists and array based lists. When your LinkedList class is finished, run the comparison method from the LinkedListTester class which performs a number of tests with your LinkedList and the Java ArrayList class. To run the method simply uncomment the line at the end of the main method in LinkedListTester that calls the comparison method. You are free to change the initial value of N for the various tests in the comparison method.

In a comment at the top of your LinkedListTester class indicate which operations are faster when using your LinkedList, which are faster when using the Java ArrayList class, and which ones are about the same.  (Realize the experiments use different starting values of N.)

For the operations tested via the experiment what do you think the Big O of each operation / method is based on the timing data? State your reasoning.

Submission: Fill in the header in the LinkedListTester.java and LinkedList.java files.. Replace <NAME> with your name. Note, you are stating, on your honor , that you did the assignment on your own.

Create a zip file name a5.zip with your LinkedListTester.java and LinkedList.java files. The zip file must not contain any directory structure, just the required files. See this page for instructions on how to create a zip via Eclipse.

Checklist. Did you remember to:

• review and follow the general assignment requirements ?
• work on the assignment individually?
• fill in the header in LinkeListTester.java and LinkedList.java?
• ensure each method in your LinkedList class states the Big O of the method in the method comment?
• ensure your version of LinkedList passes the tests in LinkedListTester.java and add your own tests?
• ensure your program does not suffer a compile error or runtime error?
• place the results of your experiments and conclusions in a comment at the top of LinkedListTester.java?
• turn in your a5.zip file with your Java source code files (LinkedList.java and LinkedListTester.java) to Canvas before 11 pm, Thursday, February 29?

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Mastering Data Structures: A Deep Dive into Linked Lists

Hey there tech enthusiasts! 👩‍💻 Today, we are going on an exciting journey into the world of Linked Lists 🚀. So grab your favorite coding snack and let’s dive right in! 💻🍿

Overview of Linked Lists

Let’s start our epic data structures quest with a brief overview of what Linked Lists are all about!

Definition of Linked Lists

Alright, buckle up folks! So, what on Earth are Linked Lists ? 🤔 Well, imagine a chain of connected nodes; each node holds some data and a reference to the next node in the sequence. It’s like a high-tech data conga line! 🕺

Types of Linked Lists

Hold on to your hats because Linked Lists come in different flavors! 🍦 We’ve got Singly Linked Lists where each node points to the next one, Doubly Linked Lists with nodes pointing to both the next and previous nodes, and even Circular Linked Lists where the last node loops back to the first! 🔄

Advantages of Linked Lists

Why bother with Linked Lists, you ask? Well, let me tell you, they come with some pretty sweet perks! 🎁

Dynamic Size

One of the coolest things about Linked Lists is that they can grow and shrink on the fly! Need more space? No problemo! Linked Lists got your back. 😉

Ease of Insertion and Deletion

Say goodbye to the headache of moving chunks of data around just to insert or delete an element! Linked Lists make adding or removing nodes as easy as pie 🥧.

Operations on Linked Lists

Time to roll up our sleeves and get our hands dirty with some Linked List operations!

Traversing a Linked List

Traversing a Linked List is like strolling through a tech park. You visit each node in turn, checking out the data and following the pointers to the next stop. It’s like a digital treasure hunt! 🧐💾

Insertion and Deletion Operations

Want to add a new node? Piece of cake! Deleting a node? It’s a breeze! Linked Lists make these operations feel like a walk in the programming park 🏞️.

Implementing Linked Lists in Programming

Now, let’s dive into the nitty-gritty of bringing Linked Lists to life in code! 💻

Creating a Linked List Class

To work with Linked Lists in your code, you’ll need a snazzy Linked List class that can handle all the node wrangling and pointer magic . Brace yourself for some serious coding action! 🤯

Performing Operations on a Linked List

Once you have your Linked List class ready, you can perform all sorts of magical operations like adding, removing, or even reversing the list! It’s like being a digital wizard casting spells on your data! 🧙‍♂️✨

Applications of Linked Lists

Linked Lists aren’t just for geeks and nerds; they have real-world applications that might surprise you! 🤓

Music Playlist Management

Ever wondered how music apps manage playlists with ease? Well, you can bet they’re using Linked Lists behind the scenes! It’s like having your favorite tunes lined up in a digital jukebox 🎵📱.

Undo Functionality in Text Editors

Ever hit that “Undo” button while typing away furiously in a text editor? Thank Linked Lists for saving you from disaster! They store your typing history like a loyal digital scribe 📝🔙.

🎉 Wrapping Up

Phew! We’ve covered a lot today, from the basics of Linked Lists to their superpowers in programming and real life. I hope you had as much fun reading this as I did writing it! 😄

Overall, Mastering Linked Lists Rocks!

Remember, Linked Lists are not just a data structure; they are like digital superheroes working behind the scenes to make our tech lives easier and more efficient. So, next time you see a Linked List, give it a digital high-five! 🙌

Thank you for joining me on this tech-tastic adventure! Stay curious, stay creative, and keep coding! Until next time, Happy Coding! 🌟🚀👩‍💻

Program Code – Mastering Data Structures: A Deep Dive into Linked Lists

### code output:, ### code explanation:.

The program starts with defining two classes: Node and LinkedList . The Node class represents each element in the list, having a data attribute to hold the value and a next attribute pointing to the next node in the list. It’s pretty much like a train, each compartment is a node, carrying data , and linked to the next compartment.

The LinkedList class represents the whole train or linked list. Its constructor initializes head , which is like the engine room of the train, leading the entire list. If head is None, that means the train hasn’t left the station yet.

The append method adds a new node (or compartment) at the end of the list. It’s like adding more capacity to the train right before departure. If the train is empty ( head is None), the new node becomes the engine room ( head ). Otherwise, it walks down the train ( while last_node.next: ) until it finds the last compartment and attaches the new one to it.

print_list method is straightforward – it goes through each node from the head to the end, printing the data. It’s akin to a conductor checking tickets in each compartment.

delete_node is where some action happens. It searches for a node with a specified key ( data ) and then unlinks it from the train. If the node to be deleted is at the head, it simply moves the head to the next node. If it’s somewhere in the middle, it finds the node, makes the previous node’s next point to the node after the one to be deleted, effectively removing it from the list.

The if __name__ == '__main__': part is like saying, ‘Let’s put this train on track and see how it runs.’ It creates a new LinkedList , adds some nodes to it, shows the list, deletes a node, and displays the list again to see the change.

So, that’s the enitre architecture of it. It challenges the concept of traditional arrays by introducing a more flexible, dynamic data structure that fits many real-world applications, like a playlist where you can add, remove, and skip songs without rearranging the whole list. Neat, isn’t it? Hope ya loved the ride! Thanks for stickin’ around. Keep coding, keep rockin’. 🚀

FAQs on Mastering Data Structures: A Deep Dive into Linked Lists

What is a linked list and how does it differ from an array.

A linked list is a linear data structure consisting of nodes where each node points to the next node in the sequence . Unlike arrays, linked lists do not have a fixed size and do not store elements in contiguous memory locations.

What are the advantages of using a linked list?

Linked lists have dynamic size, easy insertion and deletion operations, and efficient memory allocation . They are ideal for scenarios where the size of the data is not known beforehand or when frequent insertion and deletion operations are required.

What are the types of linked lists?

There are several types of linked lists, including singly linked lists, doubly linked lists, and circular linked lists. Each type has its unique way of organizing nodes and pointers.

How are linked lists different from arrays in terms of memory allocation?

Arrays allocate memory in a contiguous block, while linked lists allocate memory for each node separately, linked together by pointers. This dynamic allocation in linked lists allows for efficient memory usage.

Can a linked list contain loops or cycles?

Yes, a linked list can contain loops or cycles where a node points to a previous node, creating an infinite loop. Detecting and handling such loops is essential to avoid infinite loops and ensure proper functioning of the linked list.

What are the common operations performed on linked lists?

Common operations on linked lists include traversal, insertion, deletion, searching for a specific element, reversing the list, and detecting loops or cycles within the list.

How can one implement a linked list in a programming language?

Implementing a linked list in a programming language involves creating a node structure with data and a pointer to the next node, along with operations such as insertion, deletion, and traversal to manipulate the list effectively.

Are there any real-world applications of linked lists?

Linked lists are commonly used in memory allocation, music playlists, navigation systems, and undo functionality in text editors, showcasing their versatility and importance in various applications.

Why is mastering data structures like linked lists essential for software developers?

Understanding data structures like linked lists is crucial for software developers as they form the foundation of efficient algorithms and are fundamental to solving complex programming problems. Mastering data structures enhances problem-solving skills and prepares developers for real-world software development challenges.

Any tips for mastering the concept of linked lists?

Practice, practice, practice! Implementing linked lists in different programming languages, solving algorithmic problems involving linked lists, and visualizing the data structure will significantly aid in mastering the concept of linked lists. Remember, Rome wasn’t built in a day, so take your time to grasp the intricacies of linked lists for a solid understanding. 🚀

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cn924/CS300_Analysis_and_Design

Folders and files, repository files navigation, cs300_analysis_and_design.

2-3 Assignment: Vector Sorting Module Topics and Assignments

3-2 Assignment: Linked Lists 3-3 Milestone: Vector Data Structure Pseudocode

4-2 Assignment: Hash Tables 4-3 Milestone: Hash Table Data Structure Pseudocode

5-2 Assignment: Binary Search Tree 5-3 Milestone: Tree Data Structure Pseudocode

6-2 Submit Project One

7-1 Submit Project Two

8-1 Journal: Portfolio Submission

What was the problem you were solving in the projects for this course?

For 6-2 We were doing a runtime analysis on the different data structures we were introduced to in this class. For 7-1 We read a csv file into a C++ program and populated a binary search tree and outputted a alphabetically sorted course list with attached prerequisites.

How did you approach the problem? Consider why data structures are important to understand.

With each data structure there are advantages and disadvantages. Some work better depending on the job to be done.

How did you overcome any roadblocks you encountered while going through the activities or project?

The best way roadblocks were overcome was by preparing first. Writing out the logic, with flowcharts. pseudocode, and researching the data structure to be used for the particular job at hand.

How has your work on this project expanded your approach to designing software and developing programs?

After being introduced and working with the different types of data structures. It built confidence in working with these in the future. Also there is an over lap in other computer languages with these same type of datastructures.

How has your work on this project evolved the way you write programs that are maintainable, readable, and adaptable?

Always writing code that is commented , reusable, modular, and easily maintained should be at the forefront of any programmer's design agenda.

8-2 Discussion: Coding Challenges (Optional)

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Learn how to partition a linked list around a given value while preserving the original order of elements with our comprehensive tutorial! Whether you're new to linked list manipulation or seeking to optimize your data structures skills, understanding how to partition a linked list is essential for various algorithms and applications, such as sorting and searching.

In this tutorial, we'll delve into the intricacies of partitioning a linked list, ensuring that elements less than the given value appear before those greater than or equal to it while maintaining the original order within each partition. You'll learn how to traverse the linked list, rearrange nodes, and reconnect pointers to achieve the desired partitioning.

Join us as we unravel the complexities of linked list manipulation, providing practical examples, code snippets, and algorithmic insights along the way. From understanding the partitioning process to implementing efficient partitioning algorithms like the two-pointer approach, you'll gain the skills to tackle linked list partitioning challenges with confidence.

Ready to partition your linked lists like a pro? Dive into our tutorial now and discover how to partition a linked list around a given value while preserving the original order! For further exploration and detailed insights, don't forget to peruse the accompanying article on GeeksforGeeks: https://www.geeksforgeeks.org/partitioning-a-linked-list-around-a-given-value-and-keeping-the-original-order/

Don't miss out on the opportunity to elevate your data structures skills and master the art of linked list manipulation. Like, share, and subscribe for more tutorials and insights into data structures and algorithms. Let's partition linked lists together. Happy coding!

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Computer Science > Distributed, Parallel, and Cluster Computing

Title: distributing context-aware shared memory data structures: a case study on unordered linked list.

Abstract: In this paper, we focus on partitioning a context-aware shared memory data structure so that it can be implemented as a distributed data structure running on multiple machines. By context-aware data structures, we mean that the result of an operation not only depends upon the value of the shared data but also upon the previous operations performed by the same client. While there is substantial work on designing distributed data structures that are not context-aware (e.g., hash tables), designing distributed context-aware data structures has not received much attention. We focus on the unordered list as a case study of the context-aware data structure. We start with a shared memory context-aware lock-free unordered linked list and show how it can be transformed into a distributed lock-free context-aware unordered linked list. The main challenge in such a transformation is to preserve properties of client-visible operations of the underlying data structure. We present two protocols that preserve these properties of client-visible operations of the linked list. In the first protocol, the distribution is done in the background as a low priority task, while in the second protocol the client-visible operations help the task of distribution without affecting client latency. In both protocols, the client-visible operations remain lock-free. Also, our transformation approach does not utilize any hardware primitives (except a compare-and-swap operation on a single word). We note that our transformation is generic and can be used for other lock-free context-aware data structures.

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