wisdompeak/LeetCode
LeetCode
Ce projet est une bibliothèque curatée d'implémentations d'algorithmes et de problèmes de programmation résolus. Il sert de dépôt de référence pour la programmation compétitive et les implémentations de structures de données, fournissant des solutions optimisées pour un large éventail de défis de codage.
La collection organise les exemples de code par technique algorithmique, en se concentrant spécifiquement sur l'implémentation d'arbres, de graphes et de tas pour optimiser la complexité temporelle et spatiale. Elle fournit des solutions spécifiques au langage utilisées pour des tâches de codage haute performance.
Le dépôt couvre un large ensemble de capacités, incluant les parcours de graphes, la programmation dynamique, le traitement de motifs de chaînes et les opérations de recherche binaire. Il inclut également des implémentations pour l'interrogation de données sur plage, la manipulation de bits et la conception de structures de données personnalisées telles que des caches et des moteurs d'autocomplétion. Une couverture supplémentaire inclut les calculs mathématiques et le suivi des performances en concours.
Features
- Algorithmic References - Provides a curated collection of classic algorithmic problem-solving techniques and data structures implemented for study.
- Algorithmic Reference Implementations - Provides a curated collection of language-specific reference implementations for complex algorithms and data structures.
- Data Structure Implementations - Provides a reference library of tree, graph, and heap implementations optimized for time and space complexity.
- Hash Maps - Implements hash map strategies to identify subarrays, anagrams, and frequency patterns via efficient key-value lookups.
- Competitive Programming Repositories - Acts as a reference guide for implementing high-performance data structures and algorithms used in competitions.
- Competitive Programming Solutions - Offers optimized solutions for advanced graph theory, dynamic programming, and mathematical problems for competitive coding.
- Data Structure Study Guides - Teaches the application of trees, heaps, and linked lists through a series of solved challenges.
- Dynamic Programming Implementations - Provides optimized solutions for complex recursive relations using memoization and state-transition tables.
- Graph Traversal Algorithms - Implements graph traversal algorithms including breadth-first and depth-first search for connectivity and pathfinding.
- Prefix Trees - Implements trie data structures for autocomplete, word-break logic, and efficient string retrieval.
- Graph Connectivity Analysis - Implements graph connectivity analysis including reachability and identification of bridges and articulation points.
- LeetCode Solution References - Maintains a curated set of optimized code solutions for common LeetCode competitive programming challenges.
- Two-Pointer Techniques - Implements two-pointer techniques including sliding windows and fast-slow pointers for efficient array and string traversal.
- Bit Manipulation Solutions - Implements solutions for bit-level challenges using bitmasks and XOR operations.
- Algorithm Implementations - Serves as a comprehensive library of solved programming problems and data structure implementations.
- Binary Search Algorithms - Implements binary search algorithms for efficient lookups in sorted intervals and boundary detection.
- Range Queries - Provides implementations for range queries using segment trees and Fenwick trees for logarithmic time aggregates.
- Greedy Algorithm Solutions - Provides curated solutions for greedy algorithm challenges including interval scheduling and sequence optimization.
- Disjoint Set Union - Implements Union-Find with path compression and weights to manage connected components and minimum spanning trees.
- Linked List Manipulation Utilities - Implements fundamental linked list operations including rotation, partitioning, and node reversal.
- Memoization Caches - Implements memoization caches to optimize overlapping subproblems within dynamic programming solutions.
- String Processing Algorithms - Provides implementations of string pattern matching algorithms including KMP, Manacher, and rolling hashes.
- Tree Traversal Algorithms - Provides depth-first, breadth-first, and lowest common ancestor traversal algorithms for various tree structures.
- Technical Interview Preparation - Provides practice materials and algorithmic patterns to prepare for software engineering coding interviews.
- Algorithmic Taxonomies - Categorizes problems and solutions using a hierarchical classification system based on the underlying data structures used.
- Custom Data Structure Implementations - Builds specialized systems such as LRU caches and autocomplete engines with guaranteed time complexity.
- Language-Specific Implementations - Organizes algorithmic solutions by grouping them around specific programming languages and their native libraries.
- Sorted Container Strategies - Solves dynamic median and range-based problems using multisets and interval tracking.
- Bitwise Optimizations - Implements algorithmic techniques using bitwise operations to optimize time and space complexity for numerical problems.
- Mathematical Computing Libraries - Includes implementations for mathematical computations involving combinatorics, number theory, and geometry.
- Complexity Annotations - Provides code solutions annotated with time and space complexity for performance comparison across different approaches.
- Monotone Stack Algorithms - Implements monotonic stacks and double-ended queues to solve next-greater-element and sliding window maximum problems.
- Learning & Reference - Solutions to algorithm problems
