amejiarosario/dsa.js-data-structures-algorithms-javascript
Dsa.js Data Structures Algorithms Javascript
This project is a computer science educational resource and library providing implementations of data structures and algorithms in JavaScript. It serves as an algorithm implementation reference and a toolkit for building foundational data containers, including a collection of sorting algorithms and a guide for learning time and space complexity.
The project differentiates itself by pairing class-based implementations with Big O analysis to illustrate asymptotic complexity. It includes a non-linear data structure toolkit featuring self-balancing trees, hash maps, and graphs, alongside comparison-based sorting algorithms ranging from quadratic-time simple sorts to logarithmic-time divide-and-conquer methods.
The codebase covers a broad range of computational capabilities, including linear structures like linked lists, stacks, and queues, as well as hierarchical data modeling and recursive problem-solving patterns. It also provides tools for algorithm analysis to evaluate efficiency through operation counting and runtime growth.
Features
- Data Structure Implementations - Implements a comprehensive collection of data structures as JavaScript classes for educational reference.
- JavaScript Implementations - Implements foundational data structures and algorithms in JavaScript as an educational reference.
- Hash Maps - Implements hash maps for efficient key-value storage with average constant-time lookups.
- Non-Linear Data Structures - Provides a toolkit of self-balancing trees, hash maps, and graphs for efficient data organization.
- Algorithmic Reference Implementations - Provides modular code patterns and implementations of classic algorithms for educational study.
- Balanced Search Trees - Provides implementations of self-balancing tree structures to maintain logarithmic time complexity.
- Queue Implementations - Implements first-in-first-out queue data structures and related operations.
- Stack Data Structures - Implements last-in-first-out stack data structures with push and pop operations.
- Computer Science Education - Serves as a comprehensive educational resource for core computer science principles and complexity analysis.
- Algorithmic Problem Solving - Implements computational patterns like divide and conquer and dynamic programming for solving algorithmic challenges.
- Tree Data Structures - Designs and manages non-linear structures like binary search trees and graphs.
- Tree Map Implementations - Stores key-value pairs in sorted order using balanced trees to guarantee logarithmic lookups.
- JavaScript Implementations - Ships a library of fundamental data structures implemented specifically as JavaScript classes.
- Hierarchical Tree Structures - Implements hierarchical data structures including binary search trees, AVL trees, and graphs.
- Binary Search Trees - Implements binary search trees to maintain sorted data for efficient retrieval.
- Big O Notations - Provides tools for analyzing algorithm efficiency using Big O asymptotic notation.
- Complexity Analysis - Evaluates the scaling behavior of algorithms by mapping operation counts to Big O notation.
- Complexity Analysis - Provides tools and guides for evaluating algorithm time and space efficiency using Big O notation.
- Linked Data Structures - Provides sequential data structures using pointers to connect discrete nodes for efficient insertions.
- In-Memory Object Models - Stores internal data representations within the JavaScript runtime using in-memory object models.
- General Recursive Functions - Implements self-referencing functions for recursive sorting and tree traversal logic.
- Iterative and Recursive Traversals - Supports traversing linked lists, trees, and graphs using both iterative loops and recursive calls.
- Sequential Node Linking - Uses reference-based nodes to connect sequential data elements in linked lists.
- AVL Tree Operations - Uses node rotations in AVL trees to maintain logarithmic search complexity.
- Rotations - Implements tree balancing using rotations to ensure consistent search depth.
- Sorting and Searching Workflows - Implements and compares different sorting algorithms and search techniques for efficient data retrieval.
- Recursive Problem Solving - Employs recursive patterns and divide-and-conquer strategies to solve complex computational problems.
- In-Memory Data Structures - Maintains data structures using volatile JavaScript objects and arrays without external persistence.
- Divide and Conquer Sorts - Implements sorting algorithms that recursively split and merge sequences to reduce time complexity.
- Sorting Algorithms - Implements various comparison-based sorting algorithms including merge sort, quicksort, and bubble sort.
- Recursive Algorithms - Employs self-referencing function calls to implement tree traversal and divide-and-conquer sorting.
- Comparison-Based Sorting Implementations - Provides a library of comparison-based sorting algorithms from simple bubble sort to efficient quicksort.
- Quadratic Time Sorting - Implements simple quadratic-time sorting methods such as bubble, insertion, and selection sort.
- Algorithms and Data Structures - Data structures and algorithms implemented specifically for JavaScript developers.
