4 repositorios
Optimization techniques that solve complex problems by breaking them into overlapping subproblems and storing results.
Distinct from Knapsack Problem Solving: Candidates focus on specific problems (Knapsack) or educational guides rather than the general DP paradigm.
Explore 4 awesome GitHub repositories matching scientific & mathematical computing · Dynamic Programming. Refine with filters or upvote what's useful.
This project is a data structures and algorithms library providing a collection of fifty standard code implementations for managing data and solving common computational problems. It serves as an algorithm implementation reference and study resource for educational use. The codebase covers graph theory implementations for modeling networks and performing searches, as well as string pattern matching libraries for the retrieval of character sequences. It includes a collection of hierarchical data structures, such as binary search trees and priority heaps, and provides optimized solutions for dy
Optimizes complex calculations by storing previously computed states to avoid redundant work.
This project is a reference library of Java implementations for algorithmic coding challenges and data structure patterns. It serves as a study guide for technical interview preparation, providing a curated collection of LeetCode solutions organized by difficulty and algorithmic technique. The collection includes a mapping system that associates specific algorithm problems with the companies that frequently use them in technical interviews. The repository covers a wide range of capability areas, including tree algorithms for hierarchy construction and verification, string processing for sequ
Implements dynamic programming techniques for solving optimization problems by storing intermediate results of subproblems.
LogicStack-LeetCode is a curated repository of solved algorithm problems and data structure implementations, primarily drawn from the LeetCode platform. Its core identity is a structured collection of solutions designed to support technical interview preparation and competitive programming practice, with each solution accompanied by complexity analyses to help engineers understand performance trade-offs. The repository distinguishes itself through its breadth of coverage across fundamental algorithmic patterns and data structures. It includes implementations for array manipulation, string pro
Applies dynamic programming to partition, subset-sum, and resource-allocation challenges commonly found in technical interviews.
Este proyecto es una suite integral de implementaciones en Java para algoritmos estándar de ciencias de la computación, estructuras de datos, análisis de grafos y cálculos matemáticos. Proporciona una colección de implementaciones de referencia para contenedores de datos fundamentales, incluyendo árboles, montículos, mapas, tries y listas, junto con rutinas comunes de ordenamiento y búsqueda. La biblioteca incluye una suite especializada para análisis de redes de grafos, cubriendo caminos más cortos, árboles de expansión mínima y flujo máximo. También proporciona utilidades matemáticas para pruebas de primalidad, aritmética modular y Transformadas Rápidas de Fourier, así como herramientas de procesamiento de texto para detección de palíndromos y cálculo de distancia de edición. El código base cubre áreas de capacidad más amplias como programación dinámica para análisis de secuencias y una variedad de patrones de organización de datos utilizados para el desarrollo de software general y la educación en ciencias de la computación.
Uses dynamic programming to optimize complex sequence problems and mathematical computations.