3 repositorios
Awesome GitHub RepositoriesAlgorithm Complexity References
Documentation detailing the time and space complexity of standard library methods and common data structures.
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Awesome Algorithm Complexity References GitHub Repositories
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- trekhleb/javascript-algorithms
trekhleb/javascript-algorithms
196,089Ver en GitHubThis repository is a comprehensive collection of data structures and algorithms implemented in JavaScript, designed primarily as an educational resource for computer science study and technical interview preparation. It provides modular implementations of fundamental programming concepts, allowing developers to explore algorithmic logic and data organization through self-contained, verifiable code examples. The library distinguishes itself by pairing every implementation with formal Big O notation, providing predictable insights into time and space scaling requirements. Each algorithm is stru
Provides formal time and space complexity annotations for all implemented algorithms to aid in performance analysis.
JavaScriptalgorithmalgorithmscomputer-science - leonardomso/33-js-concepts
leonardomso/33-js-concepts
66,467Ver en GitHubThis project is a comprehensive educational repository designed to help developers master the core mechanics, runtime behaviors, and browser-native capabilities of the JavaScript language. It provides a structured knowledge base that covers fundamental language features, such as prototype-based inheritance and event-loop-based concurrency, alongside advanced topics like JIT-compiled execution and memory management. The repository distinguishes itself by offering deep-dive technical guides that bridge the gap between abstract language concepts and practical browser implementation. It features
Evaluates the time and space complexity of standard methods to inform performance-conscious architectural decisions.
JavaScriptangularconceptses6 - tsiege/tech-interview-cheat-sheet
TSiege/Tech-Interview-Cheat-Sheet
4,673Ver en GitHubEste proyecto es una colección de referencia para fundamentos de ciencias de la computación, proporcionando una guía de estudio y hojas de referencia para algoritmos y estructuras de datos. Sirve como recurso para la preparación de entrevistas técnicas, combinando conocimiento teórico con patrones de implementación prácticos para desafíos de codificación. El contenido incluye una guía comparativa para analizar la eficiencia y características de arreglos, listas enlazadas, tablas hash y árboles de búsqueda binaria. Proporciona resúmenes de conceptos académicos incluyendo complejidad temporal y espacial, métodos de ordenamiento y estrategias de búsqueda. Los materiales cubren patrones algorítmicos, implementaciones de búsqueda y ordenamiento, y notación asintótica. La información está organizada mediante una base de conocimientos basada en markdown con mapeo jerárquico de temas.
Includes a reference index detailing the time and space complexity of common data structures and algorithmic patterns.
TypeScript
