$ show labuladong/fucking-algorithm
This repository is a comprehensive collection of algorithm tutorials and solutions primarily based on LeetCode problems. It contains over 60 original articles covering various algorithm techniques and data structures, with solutions implemented in multiple programming languages including Java, Python, C++, JavaScript, and Go. The content is organized into thematic series covering dynamic programming, data structures, algorithm thinking patterns, and high-frequency interview questions. The repository serves as an educational resource for algorithm learning, providing detailed explanations of problem-solving frameworks and reusable code patterns. Each article typically includes problem descriptions, solution approaches with step-by-step reasoning, complete code implementations, complexity analysis, and links to related practice problems. The content emphasizes teaching algorithmic thinking patterns rather than just providing solutions. The codebase demonstrates solid educational value with well-structured explanations and reusable algorithm templates. However, as a documentation and education-focused project rather than a software application, it lacks traditional software engineering elements like dependency management, testing frameworks, or reproducible execution environments. The code snippets are designed for algorithm learning rather than production use.
The LLM identified 8 files worth highlighting for reuse:
Comprehensive explanation of the 0-1 knapsack problem with complete Java implementation. Demonstrates the standard dynamic programming framework: state definition, state transition equation, and space optimization. The solution shows how to derive dp[i][w] definition and build the complete DP table with proper base cases and transition logic.
Complete JavaScript implementation of a max heap with all standard operations: insert, delete, heapify, and sort. The code demonstrates proper heap maintenance with swim/sink operations and includes utility methods like isHeap() for validation. Shows how to implement a priority queue from scratch with O(log n) operations.
Implements the Fisher-Yates shuffle algorithm with four different correct variants. Includes detailed mathematical proof of correctness using the n! permutations criterion. The code demonstrates proper random number generation and element swapping with O(n) time complexity and uniform probability distribution.
Complete implementation of a monotonic queue using a doubly-linked list that maintains elements in decreasing order. Demonstrates O(1) amortized time complexity for push/pop/max operations. Shows how to solve the sliding window maximum problem efficiently with proper maintenance of queue monotonicity.
Implements a Difference class for efficient range updates on arrays. Shows how to achieve O(1) range increment operations and O(n) final array reconstruction. The pattern is useful for problems involving multiple range updates followed by point queries, with applications in scheduling and interval problems.
Demonstrates space compression technique for 2D dynamic programming problems, reducing space complexity from O(N²) to O(N). Shows the key insight of projecting 2D DP table to 1D array while maintaining correct state transitions using temporary variables to preserve needed values before overwriting.
Complete OO design of a simplified Twitter system combining hash maps, linked lists, and priority queues. Demonstrates merging k sorted lists using a priority queue to efficiently retrieve the 10 most recent tweets from followed users. Shows proper separation of concerns with User and Tweet classes.
Complete Union-Find implementation with both union by rank and path compression optimizations. Shows how to achieve near-constant time complexity for union and find operations. The code demonstrates the forest representation using parent arrays and the two key optimizations that make the algorithm efficient.