How to Master Data Structures and Algorithms in C++ with Adam Drozdek's Textbook
Data Structures and Algorithms in C++ by Adam Drozdek: A Comprehensive Review
Data structures and algorithms are fundamental concepts in computer science and software engineering. They provide the basis for designing, implementing, and maintaining efficient and reliable software systems. In this article, we will review one of the popular books on this topic, Data Structures and Algorithms in C++ by Adam Drozdek, published by Cengage Learning in 2012. We will discuss the content, strengths, weaknesses, and target audience of this book, as well as provide some FAQs for interested readers.
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What are data structures and algorithms?
Data structures are ways of organizing and storing data in a computer memory, such as arrays, lists, stacks, queues, trees, graphs, etc. Algorithms are step-by-step procedures for solving problems or performing tasks using data structures, such as searching, sorting, hashing, compression, encryption, etc.
Why are they important for software development?
Data structures and algorithms are important for software development because they affect the performance, scalability, security, and usability of software systems. Choosing appropriate data structures and algorithms can make a difference between a fast and slow program, a memory-efficient and memory-wasteful program, a secure and vulnerable program, or a user-friendly and user-unfriendly program.
What are the main features of this book?
Data Structures and Algorithms in C++ by Adam Drozdek is a comprehensive textbook that covers both the theory and practice of data structures and algorithms using the C++ programming language. The book has four main features:
It highlights the fundamental connection between data structures and their algorithms, giving equal weight to the practical implementation of data structures and the theoretical analysis of algorithms and their efficiency.
It provides abundant C++ code examples and a variety of case studies that illustrate how data structures and algorithms can be applied to real-world problems.
It includes critical new coverage of treaps, k-d trees and k-d B-trees, generational garbage collection, and other advanced topics such as sorting methods and a new hashing technique.
It provides the balance of theory and practice to prepare readers for a variety of applications in a modern, object-oriented paradigm.
The book consists of 14 chapters that cover the following topics:
Object-oriented programming using C++
This chapter introduces the basic concepts and features of object-oriented programming using C++, such as classes, objects, inheritance, polymorphism, abstract classes, templates, exceptions, etc. It also explains how to use the standard template library (STL) to implement common data structures and algorithms.
This chapter explains how to measure the time and space complexity of algorithms using the big-O notation, the big-Theta notation, and the big-Omega notation. It also discusses how to compare the efficiency of different algorithms and how to choose the best algorithm for a given problem.
This chapter covers the implementation and applications of linked lists, which are linear data structures that store data in nodes connected by pointers. It discusses the advantages and disadvantages of linked lists over arrays, and how to implement different types of linked lists, such as singly linked lists, doubly linked lists, circular linked lists, etc. It also shows how to use linked lists to implement stacks and queues.
Stacks and queues
This chapter covers the implementation and applications of stacks and queues, which are linear data structures that follow the last-in first-out (LIFO) and first-in first-out (FIFO) principles, respectively. It discusses how to use stacks and queues to solve problems such as parentheses matching, infix-to-postfix conversion, reverse Polish notation (RPN) evaluation, etc. It also shows how to implement stacks and queues using arrays or linked lists.
This chapter covers the concept and applications of recursion, which is a technique of defining a problem in terms of itself or a simpler version of itself. It discusses how to write recursive functions, how to analyze their complexity, and how to avoid common pitfalls such as infinite recursion or stack overflow. It also shows how to use recursion to solve problems such as factorial computation, Fibonacci sequence generation, binary search, etc.
This chapter covers the implementation and applications of binary trees, which are hierarchical data structures that store data in nodes with at most two children. It discusses the properties and types of binary trees, such as full binary trees, complete binary trees, balanced binary trees, binary search trees, etc. It also shows how to perform different operations on binary trees, such as traversal, insertion, deletion, searching, etc.
This chapter covers the implementation and applications of multiway trees, which are hierarchical data structures that store data in nodes with more than two children. It discusses the properties and types of multiway trees, such as general trees, B-trees, k-d trees, k-d B-trees, etc. It also shows how to perform different operations on multiway trees, such as traversal, insertion, deletion, searching, etc.
This chapter covers the implementation and applications of graphs, which are non-linear data structures that store data in vertices connected by edges. It discusses the properties and types of graphs, such as directed graphs, undirected graphs, weighted graphs, unweighted graphs, etc. It also shows how to perform different operations on graphs, such as traversal, shortest path finding, minimum spanning tree finding, topological sorting, etc.