jiayihu/pretty-algorithms
Pretty Algorithms
This project is a collection of optimized computational routines and standardized implementations of fundamental computer science algorithms. It serves as an educational library for studying and applying core algorithmic patterns, including dynamic programming, greedy strategies, and recursive decomposition, within a TypeScript environment.
The library distinguishes itself by providing generalized solvers for complex optimization and analysis tasks. It includes specific implementations for resource allocation, such as rod cutting, interval scheduling, and change-making problems, alongside sequence analysis tools like longest common subsequence and maximum subarray identification. These routines are designed to support both educational exploration and practical application in software development.
Beyond these specialized solvers, the repository covers a broad range of essential data management and processing capabilities. This includes standard procedures for sorting and searching, priority queue management, and data compression through prefix-based encoding schemes. The implementation is supported by comprehensive test suites to ensure reliability across its various algorithmic modules.
Features
- JavaScript Implementations - Provides a library of common computer science algorithms implemented in modern JavaScript for educational and production use.
- Educational Programming Resources - Acts as an educational resource for learning and applying fundamental algorithmic patterns through practical implementation.
- Data Structures and Algorithms - Serves as an educational library for studying and implementing fundamental data structures and algorithmic patterns in JavaScript.
- Longest Common - Identifies the longest sequence of elements shared between two datasets to compare differences or reconcile changes.
- Resource Allocation Solvers - Provides generalized algorithmic solvers for scheduling, cutting, and change-making problems to maximize profit or minimize costs.
- Binary Search Implementations - Provides efficient binary search implementations to locate target values within sorted collections using logarithmic time complexity.
- Sequence Analysis Tools - Provides sequence analysis tools to identify similarities and differences between datasets for reconciliation and comparison.
- Resource Allocation - Solves complex resource allocation problems to maximize efficiency under specific constraints.
- Divide And Conquer Algorithms - Implements recursive decomposition strategies to solve complex computational problems by breaking them into smaller, manageable sub-problems.
- Interval Scheduling Algorithms - Implements interval scheduling algorithms to select the maximum number of non-overlapping events from time-constrained activities.
- Logarithmic Search Algorithms - Utilizes logarithmic search algorithms to efficiently locate target values within sorted datasets by repeatedly halving the search space.
- Change-Making Algorithms - Change-Making Optimization determines the smallest set of denominations required to reach a specific target value while minimizing the total count of units used.
- Huffman Coding Implementations - Implements Huffman coding algorithms for lossless data compression using variable-length binary codes.
- Sorting and Searching Implementations - Provides optimized sorting and searching implementations to improve data retrieval speed and application performance.
- Maximum Subarray Sums - Locates the contiguous segment within a numerical sequence that yields the highest possible sum to identify peak performance periods.
- Greedy Algorithm Solutions - Implements greedy strategy patterns to solve optimization problems by making locally optimal choices at each step.
- Dynamic Programming Techniques - Provides techniques for solving complex problems by breaking them into overlapping subproblems and caching results to avoid redundant computation.
- Priority Heaps - Maintains priority-based collections using heap data structures to ensure efficient task scheduling and element access.
- Priority Queues - Manages collections of elements where items are served based on assigned priority levels rather than insertion order.
- Rod Cutting Algorithms - Calculates the most profitable way to divide a single resource into smaller parts based on individual value.
- Sorting Algorithms - Provides a suite of sorting algorithms to organize data collections into specific sequences for efficient processing.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
