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Data structures that maintain ordered pairs of values to efficiently manage state in graph traversal algorithms.
Distinct from Optimized Data Structures: Distinct from Optimized Data Structures: focuses specifically on priority-based state management for graph algorithms.
Explore 9 awesome GitHub repositories matching data & databases · Priority Queues. Refine with filters or upvote what's useful.
This project is a curated educational resource and solution repository for algorithmic challenges, specifically focused on LeetCode problems. It serves as a technical reference for common data structures and algorithmic patterns, providing verified code implementations across multiple programming languages alongside detailed logic and complexity analysis. The repository functions as a comprehensive study guide for competitive programming and technical interview preparation. It includes specialized learning tools such as an Anki flashcard dataset for spaced repetition and a browser extension t
Implements priority queues using heap structures for efficient retrieval of minimum or maximum elements.
Antigravity-Manager is an artificial intelligence model orchestration platform that functions as a unified gateway for interacting with multiple external service providers. It standardizes heterogeneous vendor data structures into a consistent internal schema, allowing third-party tools to interface with various models through a single, normalized API. The system distinguishes itself through automated infrastructure management, including the lifecycle tracking of service accounts and the secure rotation of authentication credentials. By acting as a middleware layer, it intercepts traffic to p
Routes tasks to optimal backend services based on real-time performance metrics and priority logic.
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
Data Structures maintain ordered pairs of values to efficiently manage state in graph traversal algorithms like Dijkstra.
go-datastructures is a collection of thread-safe and lock-free data structures designed for high-performance concurrent applications in Go. It provides a modular library of specialized algorithmic toolsets, including a lock-free collection library and an immutable data structure library. The project distinguishes itself through a suite of persistent AVL trees and hash array mapped tries that use branch-copying to preserve previous versions. It also implements non-blocking hash maps, queues, and tries that enable linearizable snapshots and concurrent updates without the use of mutual exclusion
Provides priority queues with floating-point keys and efficient decrease-key operations for graph algorithms.
algs4 is a Java data structures library and algorithm reference collection designed as the source code for a standard computer science textbook curriculum. It provides a comprehensive suite of fundamental implementations for sorting, searching, and core data organization. The project serves as a graph theory framework, offering tools for representing directed and undirected graphs and performing complex traversals and pathfinding. It also includes a broad sorting algorithm suite and a specialized library of Java data structures, including stacks, queues, priority queues, and symbol tables. I
Provides an indexed priority queue to efficiently retrieve and update elements associated with integer indices.
This is a collection of classical algorithms and data structures implemented as a header-only C++ library. It provides a suite of tools for general algorithm implementation, including data structure management, graph theory analysis, and string processing. The library is distinguished by its specialized toolkits for cryptographic hashing and encoding, featuring implementations of MD5, SHA-1, and Base64. It also includes advanced capabilities for high-performance string processing via suffix trees and arrays, as well as computational number theory for primality testing and arbitrary-precision
Implements priority queues using binary heaps to efficiently extract minimum or maximum items.
This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries for numerical analysis, statistics, and mathematical optimization. It serves as a foundational toolkit for developing applications in machine learning, digital signal processing, and computer vision. The framework provides specialized toolkits for training and deploying predictive models, including neural networks, support vector machines, and decision trees. It further distinguishes itself with deep integrations for real-time visual analysis, such as object tracking and facia
Implements a priority queue to manage elements based on assigned priority for efficient state management.
This library provides a collection of specialized data structures for the Swift language that extend the standard library with advanced container types. It includes implementations for double-ended queues using ring-buffers, priority queues based on min-max heaps, and memory-efficient bit-set and bit-array storage for boolean values. The project features ordered collections that maintain elements in sorted order via B-tree implementations, as well as persistent sets and dictionaries that use compressed prefix trees to share data between mutated copies. It also provides specialized containers
Implements priority queues using a min-max heap backed by an array for efficient priority-based retrieval.
This repository serves as a comprehensive library for algorithmic problem solving, providing reference implementations for fundamental computer science challenges. It is designed as a resource for technical interview preparation and competitive programming training, focusing on the mastery of common patterns and data structures required for coding assessments. The project distinguishes itself by offering solutions that emphasize idiomatic Python usage and performance optimization. It covers a wide range of algorithmic techniques, including greedy selection, dynamic programming, graph theory,
Utilizes heap-based priority queues to optimize shortest path selection in graph algorithms.