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4 dépôts

Awesome GitHub RepositoriesDynamic Programming

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.

Awesome Dynamic Programming GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • wangzheng0822/algoAvatar de wangzheng0822

    wangzheng0822/algo

    23,159Voir sur GitHub↗

    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.

    Python
    Voir sur GitHub↗23,159
  • blankj/awesome-java-leetcodeAvatar de Blankj

    Blankj/awesome-java-leetcode

    8,698Voir sur GitHub↗

    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.

    Javaalgorithmalgorithmsdatastructure
    Voir sur GitHub↗8,698
  • sharingsource/logicstack-leetcodeAvatar de SharingSource

    SharingSource/LogicStack-LeetCode

    7,495Voir sur GitHub↗

    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.

    algorithminterview-practiceinterview-questions
    Voir sur GitHub↗7,495
  • phishman3579/java-algorithms-implementationAvatar de phishman3579

    phishman3579/java-algorithms-implementation

    4,530Voir sur GitHub↗

    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.

    Javaalgorithmdata-structuresgraph
    Voir sur GitHub↗4,530
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  2. Scientific & Mathematical Computing
  3. Dynamic Programming

Explorer les sous-tags

  • Knapsack Problem Solving1 sous-tagAlgorithmic implementations of knapsack-style problems to maximize value under constraints. **Distinct from Dynamic Programming:** Focuses specifically on the knapsack optimization pattern rather than general dynamic programming techniques.
  • Linear SequencesDynamic programming applied to problems where states transition in a linear sequence. **Distinct from Dynamic Programming:** Focuses specifically on linear state transitions rather than general dynamic programming paradigms.
  • Range-Based OptimizationDynamic programming solutions focusing on contiguous sub-segments or ranges of data. **Distinct from Dynamic Programming:** Specializes in range-based optimal substructures compared to general dynamic programming.