Data Structures and Algorithms

Welcome to the Data Structures and Algorithms repository! This repository contains various implementations and solutions for fundamental data structures and algorithms in C++. The contents are meant to provide clear and concise examples as well as theoretical explanations to aid in understanding core concepts and their applications.

Table of Contents

• Introduction
• Directory Structure
  • Arrays
  • Linked Lists
  • Stacks
  • Queues
  • Trees
  • Graphs
  • Heaps
  • Tries
  • Sort Algorithms
  • Search Algorithms
  • Dynamic Programming
  • Greedy Algorithms
  • Recursion

Introduction

Data structures and algorithms are fundamental concepts in computer science that enable efficient data storage, organization, and manipulation. This repository serves as a collection of common data structures and algorithms, complete with theoretical explanations and example implementations.

Directory Structure

The repository is organized into the following folders, each containing specific implementations or examples:

Arrays

• Definition: A collection of items stored at contiguous memory locations.
• Key Operations: Access, Update, Insert, Delete.
• Use Cases: Used for storing a collection of items such as a list of objects, images, etc.

Linked Lists

• Definition: A linear data structure where elements are stored in nodes, which are connected through pointers.
• Types: Singly linked lists, doubly linked lists, circular linked lists.
• Use Cases: Useful for dynamic memory allocation, stack implementation, and maintaining a list of items with frequent insertions/deletions.

Stacks

• Definition: A linear data structure that follows the Last In First Out (LIFO) principle.
• Operations: Push, Pop, Peek.
• Use Cases: Function call management in programming languages, undo features in editors.

Queues

• Definition: A linear data structure that follows the First In First Out (FIFO) principle.
• Operations: Enqueue, Dequeue, Peek.
• Use Cases: Printer job management, task scheduling.

Trees

• Definition: A hierarchical data structure consisting of nodes with a parent-child relationship.
• Types: Binary trees, binary search trees, AVL trees, heap trees, etc.
• Use Cases: Hierarchical data representation, search and sort operations.

Graphs

• Definition: A collection of nodes (vertices) connected by edges.
• Types: Directed, Undirected, Weighted, and Unweighted graphs.
• Use Cases: Networks (social, transportation), navigational systems.

Heaps

• Definition: A special tree-based data structure that satisfies the heap property.
• Types: Max-heap, Min-heap.
• Use Cases: Priority queues, Heapsort.

Tries

• Definition: A tree-like data structure used to efficiently store a dynamic set of strings.
• Operations: Insert, Search, Delete.
• Use Cases: Autocomplete features, spell checking.

Sorting

• Definition: The process of arranging the elements of a list in a certain order.
• Common Algorithms: Bubble sort, Selection sort, Merge sort, Quick sort.
• Use Cases: Data organization, improving the efficiency of search algorithms.

Searching

• Definition: Finding an element in a data structure.
• Common Algorithms: Linear search, Binary search.
• Use Cases: Locate desired elements quickly.

Dynamic Programming

• Definition: A method for solving complex problems by breaking them down into simpler subproblems, storing the results of subproblems to avoid redundant work.
• Use Cases: Combinatorial problems, optimization problems.

Greedy Algorithms

• Definition: An approach to problem-solving that builds up a solution piece by piece, always choosing the next piece that offers the most immediate benefit.
• Use Cases: Minimal spanning trees, Huffman coding.

Recursion

• Definition: A method where the solution to a problem depends on solutions to smaller instances of the same problem.
• Use Cases: Factorial calculation, Fibonacci series generation, tree traversals.

Dive into the code, challenge yourself with the problems, and unlock the power of data structures and algorithms. Each line of code brings you one step closer to mastering the art of programming. Happy coding, and enjoy your journey through the world of algorithms be exciting and rewarding!