Welcome to the Data Structures and Algorithms repository implemented in JavaScript. This repository serves as a comprehensive guide to various data structures and algorithms, providing code examples and explanations to help you understand and implement these concepts in JavaScript.
Understanding data structures and algorithms is crucial for writing efficient and optimized code. This repository aims to provide clear explanations and JavaScript implementations for a variety of data structures and algorithms commonly used in computer science.
Arrays are one of the simplest and most widely used data structures. They store elements in contiguous memory locations, allowing for easy access and manipulation.
Linked lists consist of nodes connected by pointers. They provide dynamic memory allocation and efficient insertion and deletion operations.
A stack is a Last In, First Out (LIFO) data structure where elements are added and removed from the same end, typically the top.
Queues are a First In, First Out (FIFO) data structure where elements are added at the rear and removed from the front.
Trees are hierarchical data structures with a root element and branches. They are widely used in various applications, such as binary trees and AVL trees.
Graphs consist of nodes and edges, representing connections between nodes. They are essential for modeling relationships and dependencies.
Hash tables use a hash function to map keys to indices, providing efficient data retrieval and storage.
Heaps are specialized trees used to implement priority queues. They ensure that the highest (or lowest) priority element is always at the root.
Searching algorithms are used to find a specific element or location within a data structure. Common search algorithms include linear search, binary search, and depth-first search.
Sorting algorithms arrange elements in a specific order. Common sorting algorithms include bubble sort, quicksort, and mergesort.
Recursion involves solving a problem by breaking it down into smaller subproblems of the same type. Recursive algorithms often have a base case and a recursive case.
Dynamic programming is an optimization technique for solving problems by breaking them down into overlapping subproblems and solving each subproblem only once.
Greedy algorithms make locally optimal choices at each stage to achieve a globally optimal solution. They are often used in optimization problems.
Backtracking is a technique for solving problems by trying out different possibilities and undoing choices that do not lead to a solution.
Divide and conquer involves breaking a problem into smaller subproblems, solving them independently, and combining their solutions to solve the original problem.