# nonstriater/Learn-Algorithms

算法学习笔记

Repository: https://github.com/nonstriater/Learn-Algorithms
Canonical: https://ross.abutalabs.com/products/learn-algorithms
Language: C
License: Apache-2.0
License Family: permissive
Topics: algorithm
Last push: 2025-06-13T07:03:36+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 26, release rhythm 35, longevity 100
- inputs: {"age_days": 4503, "days_push": 446, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8983, forks 2388 (observed 2026-08-28T04:10:26.615172+00:00)

## What it is
A Chinese-language collection of algorithm and data structure study notes with C implementations, covering lists, trees, graphs, sorting, searching, and algorithm design techniques. It also includes curated job interview problem sets, big-data processing techniques, and references to algorithms used in well-known open-source projects.

## Use cases
- learn classic algorithms and data structures
- prepare for coding interview questions
- review sorting and graph algorithms
- study dynamic programming and greedy techniques
- understand big data processing algorithms like bloom filters
- find algorithm implementations in C

## When to choose
- you want structured study notes for algorithms in Chinese
- you are preparing for technical interviews at companies like those using LeetCode or Jianzhi Offer
- you prefer concise notes with C code examples

## When to avoid
- you need a production algorithm library
- you want interactive coding practice or automated grading
- you need English-language documentation

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: tutorials, education
- platform: cross-platform
- tags: algorithms, data-structures, interview-preparation, notes, chinese, c

## Member repositories
- nonstriater/Learn-Algorithms (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:26.615172+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:24:24.071512+00:00, confidence not recorded.
  - readme: https://github.com/nonstriater/Learn-Algorithms (fetched 2026-08-28T04:10:26.615172+00:00, sha 711606169e97)
- Data as of 2026-08-30T08:39:29.467469+00:00.