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nonstriater/learn-algorithms

8,936CAudience · developerComplexity · 2/5Setup · easy

TLDR

A personal collection of study notes on classic algorithms and data structures implemented in C, covering sorting, graphs, trees, and interview problems, with explanations written in Chinese.

Mindmap

mindmap
  root((learn-algorithms))
    What it does
      Algorithm study notes
      C implementations
      Interview prep
    Data Structures
      Trees and heaps
      Graphs
      Hash tables
    Algorithms
      Sorting methods
      Graph traversal
      Dynamic programming
    Use Cases
      Interview preparation
      Algorithm study
      Real-world examples
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Code map

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Things people build with this

USE CASE 1

Study implementations of classic algorithms like quicksort, Dijkstra, and dynamic programming alongside explanations in one place.

USE CASE 2

Prepare for technical interviews by browsing categorized problems covering strings, trees, graphs, and system design.

USE CASE 3

Learn how algorithms like Bloom filters and simhash are used in real-world systems like Bitcoin and Kafka.

Tech stack

C

Getting it running

Difficulty · easy Time to first run · 5min

Compile individual C files with gcc, no external dependencies or build system required.

In plain English

Learn-Algorithms is a personal collection of study notes on algorithms and data structures, written in Chinese. The author worked through a broad range of classic computer science topics and recorded their understanding as they went. The notes are implemented in C and organized into sections by topic, covering both theory and working code. The repository covers foundational data structures including linked lists, arrays, queues, hash tables, and various types of trees: binary search trees, AVL trees, red-black trees, B-trees, B+ trees, Trie trees, and heaps. Graph algorithms are also covered, including minimum spanning trees, topological sorting, shortest path methods such as Dijkstra, Floyd, and Bellman-Ford, and breadth-first and depth-first traversal. Sorting algorithms covered include bubble sort, insertion sort, selection sort, shell sort, quick sort, merge sort, heap sort, and bucket sort. The search section covers hash tables, binary search, sequential search, and dynamic search structures. A section on algorithm design techniques explains recursion, divide and conquer, dynamic programming, backtracking, greedy algorithms, and exhaustive search. There is also a collection of common interview problems organized by category: strings, stacks and queues, linked lists, numeric problems, arrays, matrix problems, binary trees, graphs, and system design. A separate section covers algorithms that appear in large-scale data processing, such as Bloom filters, inverted indexes, simhash, and MapReduce. Another section traces algorithms found in well-known open source projects like Bitcoin, Kafka, and Nginx. The README is written entirely in Chinese and includes a recommended reading list of classic algorithm textbooks and links to online coding practice platforms such as LeetCode.

Copy-paste prompts

Prompt 1
Walk me through the red-black tree implementation in the learn-algorithms C codebase, how are rotations and recoloring handled?
Prompt 2
Show me the quicksort implementation from learn-algorithms and explain how the partition step works in C.
Prompt 3
How does the Dijkstra shortest path implementation work in the learn-algorithms repository? Walk me through the code step by step.
Prompt 4
Find the dynamic programming examples in the learn-algorithms repo and explain the approach used for each one.
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