Mastering Data Structures and Algorithms in C and C++ E-Book

Sachin Naha

• Herausgeber: BookRix
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Veröffentlichungsjahr: 2023

Beschreibung

"Mastering Data Structures and Algorithms in C and C++" is a comprehensive book that serves as a guide for programmers and computer science enthusiasts to learn and understand fundamental data structures and algorithms using the C and C++ programming languages. The book is designed to help readers gain proficiency in solving complex problems and optimizing their code. The book aims to provide readers with a deep understanding of fundamental data structures and algorithms using the C and C++ programming languages. The book is designed to cater to both beginners and experienced programmers.

Leseprobe

Sachin Naha

Mastering Data Structures and Algorithms in C and C++

BookRix GmbH & Co. KG81371 Munich

Mastering Data Structures and Algorithms in C and C++

About the book

"Mastering Data Structures and Algorithms in C and C++" is a comprehensive book that serves as a guide for programmers and computer science enthusiasts to learn and understand fundamental data structures and algorithms using the C and C++ programming languages. The book is designed to help readers gain proficiency in solving complex problems and optimizing their code.

The book aims to provide readers with a deep understanding of fundamental data structures and algorithms using the C and C++ programming languages. The book is designed to cater to both beginners and experienced programmers.

Author

Contents

Introduction to Data Structures and Algorithms

Overview of basic data structures (arrays, linked lists, stacks, queues, trees, graphs, etc.)

Importance of algorithms and their efficiency analysis (time and space complexity)

Arrays and Strings  

Array manipulation and searching algorithms (linear search, binary search)

String manipulation and pattern matching (substring search, regular expressions)

Linked Lists  

Singly-linked lists, doubly linked lists, and circular-linked lists

Basic operations (insertion, deletion, traversal)

Advanced techniques (reversing, merging, detecting cycles)

Stacks and Queues  

Stack and queue implementations using arrays and linked lists

Applications of stacks and queues in solving real-world problems

Trees and Binary Trees  

Binary tree representation and traversal algorithms (in-order, pre-order, post-order)

Binary search trees and their operations (insertion, deletion, searching)

Balanced trees (AVL trees, Red-Black trees)

Graphs and Graph Algorithms  

Graph representation (adjacency matrix, adjacency list)

Graph traversal algorithms (DFS, BFS)

Shortest path algorithms (Dijkstra's algorithm, Bellman-Ford algorithm)

Minimum spanning tree algorithms (Prim's algorithm, Kruskal's algorithm)

Sorting and Searching Algorithms  

Bubble sort, selection sort, insertion sort, merge sort, quicksort, etc.

Searching techniques (linear search, binary search)

Dynamic Programming  

Understanding the concept of overlapping subproblems and optimal substructure

Solving problems using dynamic programming (e.g., Fibonacci series, knapsack problem)

Hashing  

Hash functions and collision resolution techniques

Hash tables and their implementation

Advanced-Data Structures  

Heaps and priority queues

Trie data structure

Segment trees and Fenwick trees (Binary Indexed Trees)

Bit Manipulation  

Bitwise operations and their applications

Bit manipulation tricks for optimizing algorithms

Advanced Algorithms  

Divide and Conquer

Backtracking

Greedy algorithms

Randomized algorithms

String algorithms (KMP algorithm, Rabin-Karp algorithm)

Parallel algorithms

Approximation algorithms

Interview Questions and Answers

Introduction to Data Structures and Algorithms

Data Structures and Algorithms form the backbone of computer science and play a crucial role in solving complex problems efficiently. Whether you are a beginner or an experienced programmer, understanding data structures and algorithms is essential to becoming a proficient software developer. This comprehensive guide aims to provide a step-by-step approach to mastering data structures and algorithms in C and C++, two of the most widely used programming languages in the world.

Overview of Basic Data Structures

1.1 Arrays: Arrays are one of the simplest and most fundamental data structures. They are collections of elements of the same data type, stored in contiguous memory locations. Accessing elements in an array is done using their index, and arrays offer constant-time access, making them efficient for random access. However, their size is fixed once declared, leading to limitations in dynamic data handling.

1.2 Linked Lists: Linked lists overcome the limitations of arrays by offering dynamic memory allocation. They consist of nodes, where each node contains both data and a pointer to the next node in the list. This flexibility allows for easy insertion and deletion of elements, but accessing an element at a specific position requires traversing the list, making it less efficient for random access.

1.3 Stacks: Stacks are a Last-In-First-Out (LIFO) data structure, resembling a pile of plates. Elements are inserted and removed from the same end, called the top. Common operations include push (adding an element) and pop (removing the top element). Stacks are widely used in programming, especially in recursion and parsing.

1.4 Queues: Queues are a First-In-First-Out (FIFO) data structure, resembling a line of people waiting. Elements are inserted at the rear and removed from the front. Common operations include enqueue (adding an element) and dequeue (removing the front element). Queues are useful in scheduling and resource management.

1.5 Trees: Trees are hierarchical data structures consisting of nodes connected by edges. The topmost node is called the root, and nodes with no children are called leaves. Trees are versatile and can be used to represent various hierarchical relationships, such as binary trees, binary search trees, AVL trees, and heaps.

1.6 Graphs: Graphs are collections of nodes (vertices) and edges that connect pairs of nodes. They can be directed (edges have a specific direction) or undirected. Graphs find applications in network routing, social networks, and various optimization problems.

Importance of Algorithms and Efficiency Analysis

Algorithms are step-by-step procedures or instructions for solving problems and achieving specific tasks. They are the heart of computational processes and determine the efficiency of a program in terms of time and space complexity.

2.1 Time Complexity: Time complexity measures the amount of time an algorithm takes to complete as a function of the input size. It helps us understand how the algorithm's performance scales with increasing input data. Common notations for time complexity include Big O (O), Omega (Ω), and Theta (Θ).

Big O notation (O): It represents the upper bound of an algorithm's running time. For example, an algorithm with O(n) time complexity means its execution time increases linearly with the input size (n).

Omega notation (Ω): It represents the lower bound of an algorithm's running time.

Theta notation (Θ): It gives both the upper and lower bounds, indicating a tight bound on the running time.