15 个仓库
Calculates the optimal selection of items with specific weights and values to maximize total value within capacity constraints.
Distinct from Algorithmic Problem Solving: Focuses on knapsack-specific DP, distinct from general algorithmic problem solving.
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This project is a comprehensive, community-maintained knowledge base and toolkit designed for competitive programming. It serves as a centralized repository for algorithmic theory, data structures, and mathematical techniques, providing a structured reference for informatics and collegiate programming competitions. The project distinguishes itself by integrating educational content with a robust suite of automation utilities. It provides a complete workflow for competitive programming, including tools for automated test case generation, solution verification, and direct interaction with onlin
Implements classic and advanced knapsack algorithms to solve resource allocation and optimization problems.
This project is an algorithm template library and coding interview study guide providing reusable code patterns for common data structures and algorithms. It serves as a reference for optimized strategies and a structured learning path to build proficiency in algorithmic problem solving and competitive programming. The library focuses on standardized implementations of key algorithmic patterns, including sliding windows, backtracking, dynamic programming, and binary search. It provides specific templates for managing binary search trees, searching rotated sorted arrays, and executing divide-a
Implements knapsack-specific dynamic programming to maximize value within capacity constraints.
This project is a comprehensive reference for algorithms and data structures used to solve complex computational problems in competitive programming. It serves as a technical resource for implementing advanced mathematical programming, computational geometry, and graph theory. The repository provides detailed implementation guides for diversifying algorithmic techniques, including top-down and bottom-up dynamic programming optimization, number theory, and linear algebra. It features specific guides for complex tasks such as constructing planar graphs, solving linear Diophantine equations, and
Provides algorithms for solving knapsack problems with various constraints and quantities.
This project is a collection of reference implementations for algorithms, mathematics, cryptography, compression, and machine learning written in C#. It serves as an educational library providing standard implementations of sorting, searching, and graph theory algorithms. The repository covers a wide range of computational domains, including combinatorial optimization for constraint satisfaction and scheduling, as well as symmetric and classical cryptographic ciphers. It also provides reference code for lossless data compression techniques and fundamental machine learning primitives such as r
Provides solvers for the knapsack problem to determine the most valuable item combination within a weight limit.
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
Provides curated hash table solutions with difficulty ratings and complexity analyses for interview preparation.
Includes a benchmark example that solves the knapsack problem using genetic operators on set-based representations.
This project is a comprehensive collection of C++ libraries and toolkits providing reference implementations for data structures, graph algorithms, and bitwise logic. It serves as a C++ algorithm reference containing over 180 solved coding problems and a specialized toolkit for competitive programming. The repository distinguishes itself through extensive low-level bit manipulation libraries for parity checks, endianness detection, and XOR-based logic. It also provides a wide array of reference solutions for complex algorithmic challenges involving backtracking, graph theory, and dynamic prog
Solves the 0-1 Knapsack problem using dynamic programming to maximize value within weight limits.
Algodeck is an open-source collection of flash cards designed for reviewing algorithms, data structures, and system design concepts, specifically curated for technical interview preparation. The project organizes knowledge into atomic question-and-answer pairs and incorporates spaced repetition scheduling to optimize long-term memory retention. The flash card catalog covers a broad range of computer science topics, including classic sorting algorithms like quicksort and mergesort, data structure operations for arrays, trees, heaps, tries, and graphs, as well as bit manipulation techniques for
Breaks a problem into overlapping subproblems and caches their results to avoid redundant computation.
CloudEvents is an open specification for describing event data in a common format across cloud platforms and services. It defines a standard structure and set of metadata attributes for events, enabling interoperability across different systems so producers and consumers can exchange events without custom translation. The specification provides a protocol-agnostic serialization framework that maps CloudEvents attributes and payloads to multiple serialization formats including JSON, Avro, and Protobuf, and defines transport bindings for mapping events onto protocols like HTTP, AMQP, Kafka, MQTT
Defines how to number events within partitions for gap, duplicate, and ordering detection.
InterviewGuide is a comprehensive technical interview preparation platform that covers the full spectrum of software engineering recruitment, from foundational computer science concepts through to offer negotiation. It provides structured learning paths across algorithms, operating systems, databases, networking, and programming languages, with a particular emphasis on C++ and Go. The platform aggregates real interview experiences and company-specific questions from major tech employers, offering candidates a searchable database of past written exam problems and detailed accounts of actual int
Presents a puzzle for planning trips to move a perishable load while consuming some for fuel.
This repository is a curated guide and implementation library of coding patterns used to solve data structures and algorithms problems. It serves as a technical interview study resource, providing a comprehensive set of strategies and computational logic examples for optimizing time and space complexity. The project focuses on standardized algorithmic patterns, including sliding windows, two pointers, and dynamic programming. It features specific implementations for a wide range of challenges, such as LeetCode problem solutions and specialized techniques like cyclic sort and bitwise XOR opera
Implements logic to determine if numerical sets can be partitioned into subsets meeting specific sum requirements.
MoreLINQ is a functional programming toolkit and extension library for .NET that augments LINQ to Objects with advanced operators for sequence manipulation and analysis. It provides a set of tools for declarative data transformation, leveraging lazy evaluation and composition to handle complex object sequences. The library is distinguished by its specialized capabilities for combinatorial generation, including the production of permutations, subsets, and Cartesian products. It also provides advanced sequence joining options, such as full, left, and right outer joins, and supports complex data
Splits sequences into multiple sets based on predicates or keys.
This project is a Go algorithm implementation library and a reference for data structures. It serves as a collection of solved coding interview problems and an algorithmic pattern collection, providing a reference of over 100 common challenges implemented in Go. The library focuses on specific problem-solving strategies, including sliding windows, two pointers, and dynamic programming. It provides coded examples of standard sorting, searching, and graph traversal techniques to facilitate the study of algorithmic patterns. The repository covers a broad range of capabilities, including array a
Determines if two distinct elements in a list add up to a target value using a hashmap for constant-time lookups.
此仓库是算法问题解决的综合库,提供计算机科学基础挑战的参考实现。它旨在作为技术面试准备和竞赛编程训练的资源,专注于掌握编码评估所需的常见模式和数据结构。 该项目通过提供强调符合习惯的 Python 用法和性能优化的解决方案而脱颖而出。它涵盖了广泛的算法技术,包括贪心选择、动态规划、图论和二分查找,同时提供关于分析执行复杂度以识别特定任务最高效逻辑的指导。 除了核心算法外,该集合还包括栈、队列和邻接表图表示等标准数据结构的实现。它还提供了集成外部 Web 服务和管理结构化数据的示例,确保了竞赛环境和实际软件开发所需的广泛技术技能覆盖。
Solves sequence-based optimization problems using dynamic programming tables.
This repository serves as a comprehensive resource for competitive programming and technical interview preparation. It provides a structured collection of source code implementations for fundamental data structures and classic algorithmic problems, designed to help developers master core computer science concepts and efficient coding strategies. Beyond standard problem-solving, the project distinguishes itself by integrating software design patterns into its algorithmic implementations. It demonstrates how to apply structural and behavioral patterns—such as decorators, observers, and singleto
Determines optimal subset sums by splitting inputs and merging results to reduce complexity.