File Name: data structures and algorithms in cpp .zip
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Our Data Structure tutorial is designed for beginners and professionals. The data structure name indicates itself that organizing the data in memory. There are many ways of organizing the data in the memory as we have already seen one of the data structures, i.
In later chapters, the book explains the basic algorithm design paradigms, such as the greedy approach and the divide-and-conquer approach, which are used to solve a large variety of computational problems.
Finally, you will learn the advanced technique of dynamic programming to develop optimized implementations of several algorithms discussed in the book. This chapter describes the importance of using the right data structures in any application.
The management of data is one of the most important considerations to bear in mind while designing any application.
The purpose of any application is to get some data as input, process or operate on it, and then provide suitable data as output. For example, let's consider a hospital management system. Here, we could have data about different doctors, patients, and archival records, among other things. The hospital management system should allow us to perform various operations, such as admit patients, and update the joining and leaving of doctors of different specialties.
While the user-facing interface would present information in a format that is relevant to the hospital administrators, internally, the system would manage different records and lists of items. A programmer has at their disposal several structures to hold any data in the memory. The choice of the right structure for holding data, also known as a data structure , is crucial for ensuring reliability, performance, and enabling the required functionalities in the application.
Besides the right data structures, the right choice of algorithms to access and manipulate the data is also necessary for the optimal behavior of the application. This book shall equip you with the ability to implement the right data structures and algorithms for your application design, in order to enable you to develop well-optimized and scalable applications.
We will look at their individual designs, pros, and cons. We will also implement said structures with the help of exercises. Understanding these data structures will help you to manage data in any application in a more performant, standardized, readable, and maintainable way. Linear data structures can be broadly categorized as contiguous or linked structures.
Let's understand the differences between the two. Before processing the data in any application, we must decide how we want to store data. The answer to that question depends on what kind of operations we want to perform on the data and the frequency of the operations. We should choose the implementation that gives us the best performance in terms of latency, memory, or any other parameter, without affecting the correctness of the application. A useful metric for determining the type of data structure to be used is algorithmic complexity, also called time complexity.
Time complexity indicates the relative amount of time required, in proportion to the size of the data, to perform a certain operation. Thus, time complexity shows how the time will vary if we change the size of the dataset. The time complexity of different operations on any data type is dependent on how the data is stored inside it. Data structures can be divided into two types: contiguous and linked data structures. We shall take a closer look at both of them in the following sections.
As mentioned earlier, contiguous data structures store all the elements in a single chunk of memory. The following diagram shows how data is stored in contiguous data structures:. In the preceding diagram, consider the larger rectangle to be the single memory chunk in which all the elements are stored, while the smaller rectangles represent the memory allocated for each element.
An important thing to note here is that all the elements are of the same type. Hence, all of them require the same amount of memory, which is indicated by sizeof type. The address of the first element is also known as the Base Address BA. In this case, we can always access any element using the formula instantly, regardless of the size of the array.
Hence, the access time is always constant. This is indicated by O 1 in the Big-O notation. The two main types of arrays are static and dynamic. A static array has a lifetime only inside its declaration block, but a dynamic array provides better flexibility since the programmer can determine when it should be allocated and when it should be deallocated. We can choose either of them depending on the requirement. Both have the same performance for different operations.
Since this array was introduced in C, it is also known as a C-style array. Here is how these arrays are declared:. A static array is aggregated, which means that it is allocated on the stack, and hence gets deallocated when the flow goes out of the function. On the other hand, a dynamic array is allocated on a heap and stays there until the memory is freed manually.
Since all the elements are present next to each other, when one of the elements is accessed, a few elements next to it are also brought into the cache. Hence, if you want to access those elements, it is a very fast operation as the data is already present in the cache. This property is also known as cache locality. Although it doesn't affect the asymptotic time complexity of any operations, while traversing an array, it can give an impressive advantage for contiguous data in practice.
Since traversing requires going through all the elements sequentially, after fetching the first element, the next few elements can be retrieved directly from the cache. Hence, the array is said to have good cache locality. Linked data structures hold the data in multiple chunks of memory, also known as nodes, which may be placed at different places in the memory. The following diagram shows how data is stored in linked data structures:. In the basic structure of a linked list, each node contains the data to be stored in that node and a pointer to the next node.
The last node contains a NULL pointer to indicate the end of the list. To reach any element, we must start from the beginning of the linked list, that is, the head, and then follow the next pointer until we reach the intended element. So, to reach the element present at index i , we need to traverse through the linked list and iterate i times.
Hence, we can say that the complexity of accessing elements is O n ; that is, the time varies proportionally with the number of nodes. If we want to insert or delete any element, and if we have a pointer to that element, the operation is really small and quite fast for a linked list compared to arrays. Let's take a look at how the insertion of an element works in a linked list. The following diagram illustrates a case where we are inserting an element between two elements in a linked list:.
In this way, the new node becomes part of the linked list. Similarly, if we want to remove any element, we just need to rearrange the links so that the element to be deleted is no longer connected to any of the list elements. Then, we can deallocate that element or take any other appropriate action on it. A linked list can't provide cache locality at all since the elements are not stored contiguously in memory.
Hence, there's no way to bring the next element into the cache without actually visiting it with the pointer stored in the current element. So, although, in theory, it has the same time complexity for traversal as an array, in practice, it gives poor performance.
The following section provides a summary of the comparison of contiguous and linked data structures. The following table briefly summarizes the important differences between linked and contiguous data structures in general:. The following table contains a summary of the performance of arrays and linked lists regarding various parameters:. For any application, we can choose either data structure or a combination of both, based on the requirements and the frequencies of the different operations.
Array s and linked lists are very common and are extensively used in any application to store data. Hence, the implementation of these data structures must be as bug-free and as efficient as possible.
We will see some of them in more detail in upcoming sections. Though C-style arrays do the job, they are not commonly used. There are a number of limitations that indicate the necessity of better solutions. Some of the major limitations among those are as follows:. In the following example, we will declare std::array of int of size 10 , set the value of any of the elements, and then print that value to make sure it works:.
As we can see, std::array provides operator , which is same as the C-style array, to avoid the cost of checking whether the index is less than the size of the array. Additionally, it also provides a function called at index , which throws an exception if the argument is not valid.
In this way, we can handle the exception in an appropriate manner. So, if we have a piece of code where we will be accessing an element with a bit of uncertainty, such as an array index being dependent on user input, we can always catch the error using exception handling, as demonstrated in the following example.
Apart from that, passing std::array to another function is similar to passing any built-in data type. We can pass it by value or reference, with or without const. Additionally, the syntax doesn't involve any pointer-related operations or referencing and de-referencing operations. Hence, the readability is much better compared to C-style arrays, even for multidimensional arrays. The following example demonstrates how to pass an array by value:.
We can't pass an array of any other size for this function, because the size of the array is a part of the data type of the function parameter. However, if we want to have a generic function that can work with std::array of any size, we can make the size of the array templatized for that function, and it will generate code for all the required sizes of the array.
So, the signature will look like the following:. Apart from readability, while passing std::array , it copies all the elements into a new array by default. Hence, an automatic deep copy is performed. If we don't want that feature, we can always use other types, such as reference and const reference. Thus, it provides greater flexibility for programmers. In practice, for most operations, std::array provides similar performance as a C-style array, since it is just a thin wrapper to reduce the effort of programmers and make the code safer.
However, the at function provides a check on the index, and throws an exception if the index is out of range. Due to this, it is a bit slower in practice. As mentioned earlier, iterating over an array is a very common operation. So, the code for printing all the elements in an array looks like this:. In the preceding example, when we demonstrated printing out all of the elements, we iterated using an index variable, where we had to make sure that it was correctly used according to the size of the array.
A computer program is a collection of instructions to perform a specific task. For this, a computer program may need to store data, retrieve data, and perform computations on the data. A data structure is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to solve a particular problem. Learning data structures and algorithms allow us to write efficient and optimized computer programs. Programiz offers a complete series of easy to follow DSA tutorials along with suitable examples. These tutorials are targeted for absolute beginners who want to dive into the field of computer programming.
Data Structure is a way of collecting and organising data in such a way that we can perform operations on these data in an effective way. Data Structures is about rendering data elements in terms of some relationship, for better organization and storage. For example, we have some data which has, player's name "Virat" and age Here "Virat" is of String data type and 26 is of integer data type. We can organize this data as a record like Player record, which will have both player's name and age in it. Now we can collect and store player's records in a file or database as a data structure.
If you wish, you can read through a seven-page course description. A page topic summary is also available: Algorithms and data structures—topic summary. This is a collection of PowerPoint pptx slides "pptx" presenting a course in algorithms and data structures. Associated with many of the topics are a collection of notes "pdf". Some presentations may be associated with videos "V" and homework questions "Q" , possibly with answers "A". You will note that the section numbering in the notes is paralleled in the top left corner of the slides; thus, anyone watching the slides can follow along in the notes. Before we proceed with looking at data structures for storing linearly ordered data, we must take a diversion to look at trees.
Library of Congress Cataloging-in-Publication Data. Weiss, Mark Allen. Data structures and algorithm analysis in C++ / Mark Allen Weiss, Florida International.
In later chapters, the book explains the basic algorithm design paradigms, such as the greedy approach and the divide-and-conquer approach, which are used to solve a large variety of computational problems. Finally, you will learn the advanced technique of dynamic programming to develop optimized implementations of several algorithms discussed in the book. This chapter describes the importance of using the right data structures in any application. The management of data is one of the most important considerations to bear in mind while designing any application.
Overview : This module is the second part of a three-part series on introductory programming and problem solving by computing. Topics include object-oriented problem modeling with objects, classes and methods, object-oriented problem formulation and solving, data structure implementation strageties, abstraction and encapsulation of data structures, object-oriented programming constructs, APIs and class libraries, exception handling, lists, linked lists, stacks, queues, hash tables and their algorithmic design, sorting and searching methods, recursive algorithms, and Big-O notation. This module is appropriate for FoE students. Note: This introductory message will not be prominent the next time you visit this URL again.
This book presents the fundamentals of the subject and intends to provide basic concepts involved in manipulating various data structures to students in simple language with the help of small and simple algorithms. The book is suitable for use in self-study, directed study and as a class text. In this book, Information Structures empower overseeing of a lot of information productively both as far as memory and speed.
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Data Structures and Algorithm. Analysis. Third Edition (C++ Version). Clifford A. Shaffer. Department of Computer Science. Virginia Tech.
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