raywenderlich/swift-algorithm-club

Features

• Computer Science Education - Serves as a comprehensive educational resource covering core computer science principles, complexity, and efficiency.
• Algorithmic Problem Solving - Serves as an educational resource for mastering computational logic and solving classic algorithmic puzzles.
• Set Data Structures - Implements hash tables and filters to store unique elements and key-value pairs with minimal latency.
• Data Structure Implementations - Offers educational code examples demonstrating the implementation of standard linear and hierarchical data structures.
• Searching Algorithms - Implements linear and binary search methods to locate values and patterns within collections.
• Graph Algorithms - Provides educational implementations and tools for traversing connected networks and identifying shortest routes.
• Tree Data Structures - Implements binary and spatial trees to manage nested relationships and perform recursive searches.
• Algorithm Libraries - Provides a specialized library of common data structures and algorithms implemented in Swift.
• Swift Resources - Offers a practical reference and implementation guide for essential data structures using the Swift language.
• Linear Data Structures - Implements fundamental linear data structures including stacks, queues, and linked lists.
• Numerical Computing - Provides standardized algorithms for calculating divisors, permutations, and geometric distances.
• Sorting Algorithms - Implements a variety of fast and hybrid routines for organizing large datasets into specific orders.
• Data Compression Algorithms - Implements encoding techniques to reduce data storage size and improve transmission efficiency.
• Lazy Propagation Updates - Uses lazy propagation to ensure continuous subsets of data are modified in logarithmic time.
• Pathfinding - Implements pathfinding algorithms that use estimated costs to efficiently find routes between graph nodes.
• Hash Maps - Implements hash maps using bucket-based arrays and collision resolution for constant-time data retrieval.
• Mathematical Algorithms - Includes educational implementations of numerical methods and fundamental mathematical algorithms.
• Recursive Problem Solving - Demonstrates solving hierarchical problems by decomposing them into smaller, identical subproblems.
• Logarithmic Search Optimizations - Employs divide-and-conquer strategies to locate elements in sorted collections with logarithmic search time.
• Pathfinding Algorithms - Provides concrete implementations of graph traversal and pathfinding algorithms.
• Generic Data Structures - Uses Swift generics to create type-safe, reusable containers that work across different data types.
• Linked Lists - Implements node-based sequences like linked lists using references to connect data elements.
• Hybrid Sorts - Combines multiple sorting strategies to optimize average-case performance for different dataset sizes.
• General Sorting Algorithms - Implements various sorting routines to optimize the organization of large data collections.
• Interval Querying - Implements logarithmic search techniques to retrieve segments containing specific points or range aggregates.
• Range Update Propagation - Implements lazy propagation to maintain efficiency when modifying continuous subsets of tree data.
• Data Structures - Educational algorithms and data structures.
• Computer Science Foundations - Comprehensive collection of algorithms and data structures.

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