# matthewsamuel95/ACM-ICPC-Algorithms

Algorithms used in Competitive Programming

Repository: https://github.com/matthewsamuel95/ACM-ICPC-Algorithms
Canonical: https://ross.abutalabs.com/products/acm-icpc-algorithms
Language: C++
License Family: other
Topics: acm, icpc, programming, acm-icpc, algorithm, algorithms, algorithm-competitions, acm-icpc-handbook
Last push: 2022-07-04T07:38:09+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3238, "days_push": 1521, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2307, forks 1286 (observed 2026-08-28T04:06:35.830196+00:00)

## What it is
A curated collection of algorithms and data structures used in competitive programming, primarily for the ACM-ICPC contest. Implementations are provided in multiple languages including C++, Java, Python, C, and Kotlin.

## Use cases
- prepare for ACM-ICPC competitive programming contests
- study classic algorithms and data structures with example implementations
- practice coding interview problems
- find reference implementations of graph, tree, and backtracking algorithms
- learn bit manipulation techniques

## When to choose
- you are training for ICPC or similar algorithmic contests
- you want multilingual reference implementations of standard algorithms
- you are a student learning data structures and algorithms

## When to avoid
- you need a production-ready algorithm library with tests and packaging
- you need actively maintained, optimized code for a software project
- you require a license permitting redistribution

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: education, programming-languages
- platform: cross-platform
- tags: competitive-programming, acm-icpc, data-structures, interview-prep, algorithm-collection, algorithms

## Member repositories
- matthewsamuel95/ACM-ICPC-Algorithms (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:35.830196+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 2026-08-30T02:39:39.723143+00:00, confidence not recorded.
  - readme: https://github.com/matthewsamuel95/ACM-ICPC-Algorithms (fetched 2026-08-28T04:06:35.830196+00:00, sha 9b736ff63673)
- Data as of 2026-08-30T08:39:29.467469+00:00.