4 dépôts
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.
This project is a comprehensive suite of Java-based implementations for standard computer science algorithms, data structures, graph analysis, and mathematical computations. It provides a collection of reference implementations for fundamental data containers, including trees, heaps, maps, tries, and lists, alongside common sorting and searching routines. The library includes a specialized suite for graph network analysis, covering shortest paths, minimum spanning trees, and maximum flow. It also provides mathematical utilities for prime testing, modular arithmetic, and Fast Fourier Transform
Uses dynamic programming to optimize complex sequence problems and mathematical computations.